Predictive Framework for Imbalance Dataset

Predictive Framework for Imbalance Dataset

The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-pro...

Full description

Saved in:
Bibliographic Details
Main Author: Megat Norulazmi, Megat Mohamed Noor
Format: Thesis
Language:eng
eng
Published: 2012
Subjects:
HA Statistics
Online Access:https://etd.uum.edu.my/3603/1/s91447.pdf
https://etd.uum.edu.my/3603/7/s91447.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uum-etd.3603
record_format uketd_dc
institution Universiti Utara Malaysia
collection UUM ETD
language eng
eng
advisor Jusoh, Shaidah
topic HA Statistics
spellingShingle HA Statistics
Megat Norulazmi, Megat Mohamed Noor
Predictive Framework for Imbalance Dataset
description The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials.
format Thesis
qualification_name Ph.D.
qualification_level Doctorate
author Megat Norulazmi, Megat Mohamed Noor
author_facet Megat Norulazmi, Megat Mohamed Noor
author_sort Megat Norulazmi, Megat Mohamed Noor
title Predictive Framework for Imbalance Dataset
title_short Predictive Framework for Imbalance Dataset
title_full Predictive Framework for Imbalance Dataset
title_fullStr Predictive Framework for Imbalance Dataset
title_full_unstemmed Predictive Framework for Imbalance Dataset
title_sort predictive framework for imbalance dataset
granting_institution Universiti Utara Malaysia
granting_department Awang Had Salleh Graduate School of Arts & Sciences
publishDate 2012
url https://etd.uum.edu.my/3603/1/s91447.pdf
https://etd.uum.edu.my/3603/7/s91447.pdf
_version_ 1747827611150581760
spelling my-uum-etd.36032019-11-13T00:03:05Z Predictive Framework for Imbalance Dataset 2012 Megat Norulazmi, Megat Mohamed Noor Jusoh, Shaidah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HA Statistics The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials. 2012 Thesis https://etd.uum.edu.my/3603/ https://etd.uum.edu.my/3603/1/s91447.pdf text eng validuser https://etd.uum.edu.my/3603/7/s91447.pdf text eng public http://sierra.uum.edu.my/record=b1250825~S1 Ph.D. doctoral Universiti Utara Malaysia Abe, H., Ohsaki, M., Yokoi, H., & Yamaguchi, T. (2006). New Frontiers in Artificial Intelligence. Springer Berlin/ Heidelberg. Alpaydin, E. (2010). Introduction to Machine Learning. The MIT Press,. Aÿfalg, J. (2008). Advance Analysis on Temporal Data. Dissertation of Mathematik, Informatik and Statistik, Munchen. Bach, F. R., Heckerman, D., & Horvitz, E. (2005). On the path to an ideal ROC curve: Considering cost asymmetry in learning classifiers., (pp. 9-16). Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl., 6(1), 20-29. Bauer, E., & Kohavi, R. (1999). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning, 36 (1), 105-139. Beck, J. R., & Schultz, E. K. (1986.). The use of ROC curves in test performance evaluation. Arch Pathol, 110: 13–20. Begg, C.D., Merdes, T., Byington, C., & Maynard, K. (1999). Dynamics Modeling for Mechanical Fault Diagnostics and Prognostics. Proceedings of MARCON 99, Maintenance and Reliability Conference (p. 22). Gatlinburg, Tennessee. Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kotter , T., Meinl, T., et al. (2007). KNIME: The Konstanz Information Miner. Studies in Classification, Data Analysis , and Knowledge Organization (GfKL 2007). Springer. Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987). Occams Razor. Inf. Process. Lett. , 24, 377-380. Bockhorst, J., & Craven, M. (2005). Markov networks for detecting overlapping elements in sequence data. Neural Information Processing Systems 17. MIT Press. Bousquet, O., Boucheron, S., & Lugosi, G. (2004). Introduction to Statistical Learning Theory. (pp. 169--207) . Springer. Bradley, A.P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, 1145-1159. Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science. Breiman, L., & Spector, P. (1992). Submodel selection and evaluation in regression: The X-random case. International Statistical Review, 291-319. Breiman, L., Friedman, J.H., Olshen, R.A., & Stone, C.J. (1984). Classification and Regression Trees. Chapman & Hall, New York, NY. Bunescu, R., Ge, R., Kate, R.J., Marcotte, E.M., Mooney, R. J., Ramani, A.K., et al. (2004). Comparative Experiments on Learning Information Extractors for Proteins and their Interactions. Campos, M., Palma, J., & Marín, R. (2007). Temporal Data Mining with Temporal Constraints. 4594, pp. 67-76. Cardie, C., & Howe, N. (1997). Improving Minority Class Prediction Using Case-Specific Feature Weights. (pp. 57-65) . Morgan Kaufmann. Caruana, R. (2004, September 15). Performance Measures. Retrieved October 20, 200, from cs.cornell.edu: http://www.cs.cornell.edu/Courses/cs578/2003fa/performance_ measures.pdf Chan, K.-P., & Fu, A.W. (1999). Efficient Time Series Matching by Wavelets., (pp. 126-133). Chan, P.K., & Stolfo, S.J. (1998). Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. Knowledge Discovery and Data Mining (pp. 164-168). AAAI Press. Chapple, M. (2010, June 11). Data Mining: An Introduction. Retrieved September 20, 2010, from databases.about.com: http://databases.about.com/od/datamining/a/datamining.htm Chawla, N.V., Bowyer, K.W., Hall, L.O., & Kegelmeyer, W.P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. Chawla, N.V., Japkowicz, N., & Kotcz, A. (2004). Editorial: special issue on learning from imbalanced datasets. SIGKDD Explor. Newsl., 6, 1-6. Chew, C. (2006).Media Defect Library. Fuji Electric Malaysia Choi, M.-K., & Kim, H.-M. (1999). A Study on Process Control to Improve Yield in Semiconductor Manufacturing., (p. 1215-1219). Cieslak, D.A., Chawla, N.V., & Striegel, A. (2006). Combating imbalance in network intrusion datasets. In Proocedings of the IEEE International Conference on Granular Computing, (pp. 732–737). Cohen, G., Hilario, M., Sax, H., Hugonnet, S., & Geissbuhler, A. (2006). Learning from imbalanced data in surveillance of nosocomial infection. Artificial Intelligence in Medicine, 37(1):7–18. Cohen, P.R. (1995). Empirical methods for artificial intelligence. IEEE Expert: Intelligent Systems and Their Applications, Vol 11 issue 6 pp 88. Collier, K., & Held, G. (2000). Data Mining Quality in Manufacturing Data. SAS & KPMG Consulting, (Best Practices Approach To The Manufacturing Industry), 24. Compumine. (2008). Evaluating a classification model–What does precision and recall tell me? Retrieved April 14, 2010, from Compumine: http://www.compumine.com/web/public/ newsletter/20071/precision-recall Cotofrei, P., & Stoffel, K. (2005). First-Order Logic Based Formalism for Temporal Data Mining. In T.Y. Lin, S. Ohsuga, C.-J. Liau, X. Hu, & S. Tsumoto, Foundations of Data Mining and knowledge Discovery (Vol.6/2005, pp.185-210). Springer Berlin / Heidelberg. Das, G., Lin, K.-i., Mannila, H., Renganathan, G., & Smyth, P. (1998). Rule Discovery From Time Series. KDD (pp. 16-22) . AAAI Press. Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning (pp. 233-240). Pittsburgh, Pennsylvania: ACM. Davis, J., Burnside, E., Dutra, I., Page, D., Ramakrishnan, R., Costa, V.S., et al. (2005). View learning for statistical relational learning: with an application to mammography. Proceedings of the 19th international joint conference on Artificial intelligence (pp. 677--683). Edinburgh, Scotland: Morgan Kaufmann Publishers Inc. Devroye, L., Györfi, L., & Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. New-York: Springer–Verlag. Dietterich, T.G. (1997). Machine Learning Research: Four Current Directions. AI Magazine, 18, 97-136. Divyakant, Y.-L. W., Agrawal, D., & Abbadi, A.E. (2000). A Comparison of DFT and DWT Based Similarity Search in Time-Series Databases., (pp. 488-495). Donkers, H. (2006, July 3). Data mining and the knowledge discovery process. Retrieved October 20, 2010, from cs.unimaas.nl: www.cs.unimaas.nl/datamining/2006/material/introduction2006. ppt Drummond, C., & Holte, R.C. (2000).Explicitly Representing Expected Cost: An Alternative to ROC Representation. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 198- -207). ACM Press. Drummond, C., & Holte, R. (2003). C4.5, class imbalance, and cost sensitivity: why under-sampling beats oversampling . n Proc. Workshop on Learning from Imbalanced Datasets II, (pp. 1-8). Dupret, Y., & Kielbasa, R. (2004). Modeling Semiconductor Manufacturing Yield by Test Data and Partial Least Squares. 16th International Conference on Microelectronics (pp. 134- 137). Tunisia: IEEE. Estad Software. (2010). What are the most used data mining techniques? Retrieved December 1, 2011, from http://www.apa.org/ethics/code.html Estabrooks, A., & Japkowicz, N. (2001).A Mixture-of-Experts Framework for Learning from Imbalanced Datasets. Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. 2189, pp. 34-43. London, UK: Springer Berlin / Heidelberg. Evgeniou, T., Pontil, M., & Poggio, T. (2000). Statistical Learning Theory: A Primer. Int. J. Comput. Vision, 38(1), 9- 13. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recogn. Lett., 27(8), 861-874. Fayyad, U. M. (1997). Data Mining and Knowledge Discovery in Databases: Implications for Scientific Databases. Proceedings of the Ninth International Conference on Scientific and Statistical Database Management (pp. 2-11). Washington, DC, USA: ACM. Flach, P.A. (2003).The geometry of ROC space: understanding machine learning metrics through ROC isometrics. in Proceedings of the Twentieth International Conference on Machine Learning (pp. 194-201). AAAI Press. Freund, Y., Györfi, L., Turán, G., & Zeugmann, T. (2008). Algorithmic Learning Theory. In Proceedings of 19th International Conference, ALT. 5254. Budapest, Hungary: Springer. Fukunaga, K. (1990). Introduction to statistical pattern recognition (2nd ed.). San Diego, CA, USA: Academic Press Professional, Inc. Galushka, M., Patterson, D., & Rooney, N. (2006). Temporal Data MIning for Smart Homes. Designing Smart Homes, vol. 4008/2006 of Lecture Notes in Computer Science. 4008/2006, pp. 85-108. Springer Berlin/Heidelberg. Gao, S., Lee, C.H., & Lim, J.-H. (2006). An ensemble classifier learning approach to ROC optimization. 18th International Conference on Pattern Recognition (pp. 679- 682). Hong Kong, China: IEEE Computer Society. García, S., & Herrera, F. (2009).Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy. Evolutionary Computation, 17, 275-306. Gardner, M., & Bieker, J. (2000). Data mining solves tough semiconductor manufacturing problems. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 376--383). Boston, Massachusetts, United States: ACM. Gilad B. (2008, March 02). "Learning Has Just Started" -an interview with Prof. Vladimir Vapnik. Retrieved Jan 20, 2010, from learning theory: http://www.learningtheory.org/ index.php?option=com_content&view=article&id=9%3Aqlearning- has-just-startedq-an-interview-with-prof-vladimirvapnik& catid=12%3Ainterviews&Itemid=8 Goadrich, M., Oliphant, L., & Shavlik, J. (2004). Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction. Proceedings of the 14th International Conference on Inductive Logic Programming, (pp. 98-115). Porto, Portugal. Goh, L. (2003). QIS System. techreport. Grover, L.K., & Mehra, R. (2008). The Lure of Statistics in Data Mining. Journal of Statistics Education, 16. Grzymala B., J., Stefanowski, J., & Wilk, S. (2005). A Comparison of Two Approaches to Data Mining from Imbalanced Data. Journal of Intelligent Manufacturing, 16(6), 565-573. Gu, Q., Cai, Z., & Zhu, L. (2009). Classification of Imbalanced Datasets by Using the Hybrid Re-sampling Algorithm Based on Isomap. Proceedings of the 4th International Symposium on Advances in Computation and Intelligence (pp.287-296). Huangshi, China: Springer-Verlag. Guh, R.-S., Zorriassatine, F., Tannock, J.D.T., & O’Brien, C. (1999). On-line control chart pattern detection and discrimination--a neural network approach. Artificial Intelligence in Engineering, 13(4), 413-425. doi: DOI: 10.1016/S0954-1810(99)00022-9. Guo, H., & Viktor, H. L. (2004). Learning from imbalanced datasets with boosting and data generation: the DataBoost- IM approach. SIGKDD Explorations, 6, 30-39. Halevi, G. (2006).Industrial Competitiveness Cost Reduction . Springer Netherlands. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations, Volume 11, Issue 1, Volume 11, Issue 1. Hand, D. (1997). Construction and Assessment of Classification Rules. Chichester: John Wiley & Sons. Hansen, L.K., & Salamon, P. (1990). Neural Network Ensembles. IEEE Trans. Pattern Anal. Mach. Intell., 12(10) , 993-1001. Hart, P.E. (1968). The Condensed Nearest Neighbor Rule. Institute of Electrical and Electronics Engineers Transactions on Information Theory, Vol. 14, pp. 515-516. Himmel, C.D., Kim, T.S., Krauss, A., Kamen, E.W., & May, G.S. (1995). realtime predictive control of semiconductor manufacturing process using neural network. in Proceedings of the 1995 American Control Conference, 2, pp. 1240-1244. Hofemann, P. (2009, January 23). A collaborative course for HDD. Retrieved February 11, 2009, from solid-state.com: http://www.solid-state.com/displayarticle/349314/5/none/none /Dept/Acollaborative-course-for-HDDmanufacturing Hofemann, P. (2008, September 23). Hard Disk Drive Industry Driving Areal Density and Lithography. Retrieved February 11, 2009, from molecularimprints.com: http://www.molecularimprints.com/NewsEvents/techarticles/ Diskcon/Hofemann2.pdf Horner, R., El-Haram, M., & Munns, A. (1997). Building maintenance strategy: a new management approach. Journal of Quality in Maintenance Engineering, 3, 273-280. Huang, F., Xie, G., & Xiao, R. (2009). Research on Ensemble Learning. Artificial Intelligence and Computational Intelligence, International Conference on, 3, 249-252. Huang, J., Smola, A.J., Gretton, A., Borgwardt, K.M., & Scholkopf, B. (2007). Correcting Sample Selection Bias by Unlabeled Data. In Advances in neural information processing systems 19, (pp. 601-608). Hughes, G.F., Murray, J.F., Kreutz-Delgado, K., & Elkan, C. (2002). Improved disk-drive failure warnings. IEEE Transactions on Reliability, 51, 350-357. Information entropy. (2010, February 12). Retrieved September 23, 2010, from worldlingo.com: http://www.worldlingo.com/ma/enwiki/en/Information_entropy Inman, R.A. (2010, February 12). Maintenance. Retrieved November 25, 2010, from referenceforbusiness.com: http://www.referenceforbusiness.com/management/LogMar/Main tenance.html Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intell. Data Anal., 6, 429-449. Jensen, D.D., & Cohen, P.R. (2000). Multiple Comparisons in Induction Algorithms. Mach. Learn., 38(3), 309-338. John, L. (2002). Media Defect Summary. Fuji Electric Malaysia. Joshi, M.V., P. Kumar, V., & Agarwal, R.C. (2001). Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements. Proceedings of the 2001 IEEE International Conference on Data Mining (pp. 257-264). Washington, DC, USA: IEEE Computer Society. Kam, P.S., & Fu, A.W. (2000). Discovering Temporal Patterns for Interval-Based Events. Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery (pp. 317--326). London, UK: Springer-Verlag. Kang, S., Kim, J., Chae, J., Choi, W., & Lee, S. (2007). Similarity search using the polar wavelet in time series databases. Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications (pp. 1347-1354). Qingdao, China: Springer-Verlag. Karagiannopoulos, M.G., Anyfantis, D.S., Kotsiantis, S.B., & Pintelas, P.E. (2007). Local cost sensitive learning for handling imbalanced datasets. Mediterranean Conference on Control & Automation, 1-6. Kempe, S., Hipp, J., & Kruse, R. (2008). Data Analysis, Machine Learning and Applications. In F.A. Temporal, Data Analysis, Machine Learning and Applications (pp. 253-260). Springer Berlin Heidelberg. Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. SIGMOD Rec., 30(2), 151-162. Kim, O., & Kasmer, L. (2006). What Is Prediction and What Can Prediction Do to Promote Reasoning? The annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education. Kim, T.S., Ahn, S.H., Jang, Y.G., Lee, J.I., Lee, K.J., Kim, B.Y., et al. (2000). Yield prediction models for optimization of high-speedmicro-processor manufacturing processes. Electronics Manufacturing Technology Symposium, 2000. Twenty-Sixth IEEE/CPMT International (pp. 368-373). Santa Clara, CA, USA: IEEE. Kittler, R., & Wang, W. (2000). Data mining for yield improvements. In Proc. International Conference Modeling Analysis of Semiconductor Manufacturing (MASM), (pp. 270- 277). Knierim, T. (2010, June 10). Uncertainty Principle. Retrieved November 25, 2010, from thebigview.com: http://www.thebigview.com/spacetime/uncertainty.html Koc, M., & Lee, J. (2001). A system framework for next- generation e-maintenance system. Proceeding of Second International Symposium on Environmentally Conscious Design and Inverse Manufacturing. Tokyo. Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the 14th international joint conference on Artificial intelligence-Volume 2 (pp. 1137-1145). Montreal, Quebec, Canada: Morgan Kaufmann. Kok, S., & Domingos, P. (2005). Learning the structure of Markov logic networks. Proceedings of the 22nd international conference on Machine learning (pp. 441-448). Bonn, Germany: ACM. Korn, F., Jagadish, H.V., & Faloutsos, C. (1997). Efficiently supporting ad hoc queries in large datasets of time sequences. SIGMOD Rec, 26(2), 289-300. Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2006). Handling imbalanced datasets: A review. GESTS International Transactions on Computer Science and Engineering VOL.30. Kubat, M., & Matwin, S. (1997). Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. In Proceedings of the Fourteenth International Conference on Machine Learning (pp. 179-186). Morgan Kaufmann. Kubat, M., Holte, R. C., & Matwin, S. (1998). Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Mach. Learn., 30(2-3), 195-215. Kusiak, A., & Burns, A. (2005). Mining Temporal Data: A Coal-Fired Boiler Case Study. In Proceedings of the 9th International Conference, KES (pp. 953-958). Melbourne, Australia: Springer. Lamberson, P. (2008, April 2). Inductive Reasoning and Bounded Rationality. Retrieved November 26, 2010, from mit.edu: http://www.mit.edu/~pjl/page2/files/bounded_ rationality.pdf Landgrebe, T.C., Paclik, P., & Duin, R.P. (2006). Precision-recall operating characteristic (P-ROC) curves in imprecise environments. Proceedings of the 18th International Conference on Pattern Recognition-Volume 04 (pp. 123-127). Washington, DC, USA: IEEE Computer Society. Langford, J. (2005). Tutorial on Practical Prediction Theory for Classification. J. Mach.Learn. Res., 6, 273-306. Lanzi, P.L. (2006, November 25). Machine Learning and Data Mining: 10 Introduction to Classification. Retrieved November 29, 2010, from slideshare.net: http://www.slideshare.net/pierluca.lanzi/machine-learning-and-data-mining-10-introduction-to-classification Laxman, S., & Sastry, P.S. (2006). A survey of temporal data mining. SADHANA, Academy Proceedings in Engineering Sciences. Lee, J., & Wang, H. (2008).New Technologies for Maintenance . In Complex System Maintenance Handbook (pp. 49-78). Springer London. Lewis, D.D., & Gale, W.A. (1994). A Sequential Algorithm for Training Text Classifiers. Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 3-12). Dublin, Ireland: Springer-Verlag. Li, C.J., & Yoo, J. (1998). Prognosis of Gear Tooth Crack Growth. Proceedings of the 52nd Meeting of the Society of Mechanical Failures Prevention Technology (pp. 419-428). Virginia Beach, VA. Lin, C.C., & Tseng, H.-Y. (2005). A neural network application for reliability modelling and condition-based predictive maintenance. The International Journal of Advanced Manufacturing Technology, 25, 174-179. Ling, C.X., & Li, C. (1998).Data Mining for Direct Marketing : Problems and Solutions. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98) (pp. 73-79). AAAI Press. Liu, A., Martin, C., Cour, B., & Ghosh, J. (2010). Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiers. In R. a. Stahlbock, Data Mining in Annals of Information Systems, Volume 8, Part 3 (Vol. 8, pp. 159-192). Springer US. Liu, B. (2007). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer. Liu, Z., Yu, J.X., Lin, X., Lu, H., & Wang, W. (2005). Locating Motifs in Time-Series Data. In Advances in Knowledge Discovery and Data Mining (Vol. 3518/2005, pp.343- 353). Springer Berlin/Heidelberg. Lomax, R.G. (2001).An introduction to statistical concepts. New Jersey: Lawrence Erlbaum. Maloof, M.A. (2003). Learning when datasets are imbalanced and when costs are unequal and unknown. ICML-Workshop on Learning from Imbalanced Datasets II. Marquez, A.C. (2007). The Maintenance Management Framework. In Models and Methods for Complex Systems Maintenance (pp. 69-76). Springer London. McCarthy, K., Zabar, B., & Weiss, G. (2005). Does cost- sensitive learning beat sampling for classifying rare classes? Proceedings of the 1st international workshop on Utility-based data mining (pp. 69-77). Chicago, Illinois: ACM. Mende, T., Koschke, R., & Leszak, M. (2009). Evaluating Defect Prediction Models for a Large Evolving Software System. Software Maintenance and Reengineering, European Conference on, 0, 247-250. Metz, C.E. (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine, 8, 283-298. Mieno, F.S., Shibuya, T., & Odagiri, Y. (1999). Yield improvement using data mining system. IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (pp. 391-394). Santa Clara, CA, USA: IEEE Xplore. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., & Euler , T. (2006). YALE: rapid prototyping for complex data mining tasks. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 935-940). Philadelphia, PA, USA: ACM. Misra, K.B. (2008).Handbook of Performability Engineering. Springer London. Opitz, D., & Maclin, R. (1999). Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research, 11, 169-198. Partridge, D., & Yates, W.B. (1996). Engineering Multiversion Neural-Net Systems. Neural Computation, 8, 869- 893. Payne, T.R., & Edwards, P. (1998). Implicit Feature Selection with the Value Difference Metric. 13th European Conference on Artificial Intelligence (pp. 450-454). Brighton, UK: John Wiley & Sons. Peebles, T.D., Essawy, M.A., & Fein-Sabatto, S. (1999). An intelligent methodology for remaining useful life estimation of mechanical components. Proceedings of MARCON 99, Maintenance and Reliability Conference (pp.27.01-27.09). Peter. (2010, September 21 ). The law of large numbers or the flaw of averages. Retrieved September 25, 2010, from probabilitytheory.info: http://www.probabilitytheory.info/ content/item/6-the-law-of-large-numbers-/the-law-of-averages Phua, C., Alahakoon, D., & Lee, V. (2004). Minority report in fraud detection: classification of skewed data. SIGKDD Explor. Newsl., 6, 50-59. Piatetsky-Shapiro, G. (2009, November 6).Difference between Data Mining and Statistics. Retrieved October 20, 2010, from kdnuggets.com: http://www.kdnuggets.com/faq/difference- data-mining-statistics.html Pinheiro, E., Weber, W.-D., & Barroso, L.A. (2007). Failure trends in a large disk drive population. Proceedings of the 5th USENIX conference on File and Storage Technologies (pp. 2-2). San Jose, CA: USENIX Association. Powers, D. M. (2008). Evaluation Evaluation. 18th European Conference on Artificial Intelligence (pp. 843-844). Patras, Greece: IOS Press. Provost, F., & Fawcett, T. (1997). Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (pp. 43-48). AAAI Press. Provost, F., & Fawcett, T. (2001). Robust Classification for Imprecise Environments. Mach. Learn., 42(3), 203-231. Provost, F., Fawcett, T., & Kohavi, R. (1998). The Case Against Accuracy Estimation for Comparing Induction Algorithms. Proceedings of the Fifteenth International Conference on Machine Learning (pp. 445-453). San Francisco, CA, USA: Morgan Kaufmann. Raskutti, B., & Kowalczyk, A. (2004). Extreme re-balancing for SVMs: a case study. SIGKDD Explor. Newsl., 6(1), 60-69. Richards, G., & Wang, W. (2006). Empirical Investigations on Characteristics of Ensemble and Diversity. IEEE Proceedings of IJCNN06. Ricker, T. (2007, April 25). SSD prices in freefall-won't overtake hard disks anytime soon. Retrieved February 11, 2009, from engadget.com: http://www.engadget.com/2007/04/25/ ssd-prices-in-freefall-wont-overtakehard-disks-anytime-soon/ Rietman, E.A. (1997). Multi Step Process Yield Control with Large System Models. Proceedings of the 1997 American Control Conference. 3, pp. 1573-1574. Albuquerque, NM , USA : IEEE. Rijsbergen, C. (1979). Information Retrieval. Journal of the American Society for Information Science . London: Butterworths: Wiley Subscription Services, Inc., A Wiley Company. Russell, S.J., & Norvig, P. (2003).Artificial Intelligence: A Modern Approach. Pearson Education,. Sanchez, P. (2004, 04 29). Euclidean distance. Retrieved Mar 20, 2010, from planetmath.org: http://planetmath.org/ encyclopedia/EuclideanDistance.html SAS, I. (2001). Getting Started With SAS Enterprise Miner. Cary, NC: SAS Institute Inc. Satonori, I. (2002a). Equipment Maintenance. Fuji Electric Malaysia. Satonori, I. (2002b). Mechanical Texture and Cleaning. Fuji Electric Malaysia. Satonori, I. (2002c). Production Process. Fuji Electric Malaysia. Satonori, I. (2003). Quality Control. Fuji Electric Malaysia. Satonori, I. (2002d). Tester and Failure Analysis. Fuji Electric Malaysia. Schipper, J.D. (2008, Dec). A knowledge-based toxicology consultant for diagnosing multiple disorders. Doctorate dissertation. University of Florida. Schölkopf, U.V., & Bernhard. (2009). STATISTICAL LEARNING THEORY: MODELS, CONCEPTS, AND RESULTS. Seabhcan. (2005). Manhattan distance. Retrieved Feb 05, 2010, from computervision.wikia.com: http://computervision.wikia.com/wiki/Manhattan_distance Seiffert, C., Khoshgoftaar, T.M., Hulse, J.V., & Napolitano , A. (2008). Improving Learner Performance with Data Sampling and Boosting. ICTAI (1) (pp. 452-459). IEEE Computer Society. Senin, P. (2008). Dynamic Time Warping Algorithm Review. Retrieved from Science. Information and Computer Science Departament University of Hawaii, Honolulu: http://129.173. 35.31/~pf/Linguistique/Treillis/ReviewDTW.pdf Sewell, M. (2008). Ensemble Learning. Retrieved Jan 05, 2010, from machinelearning.martinsewell.com: http://machine-learning.martinsewell.com/ensembles/ensemble-learn Shao, J. (1993).Linear Model Selection by Cross-Validation. Journal of the American Statistical Association, 88,486-494. Sheu, D.D., & Kuo, J.Y. (2006). A model for preventive maintenance operations and forecasting. Journal of Intelligent Manufacturing, 17, 441-451. Shmulevich, I. (2002). Computational Learning Theory. Retrieved Feb 09, 2010, from personal.systemsbiology.net: http://personal.systemsbiology.net/ilya/LEARN.htm Silverman, L.L. (1999). Critical shift: the future of quality in organizational performance. ASQ Quality (p. 294). Milwaukee: ASQ Quality. Retrieved July 17, 2011, from http://www.mendeley.com/import/. Singla, P., & Domingos, P. (2005). Discriminative training of markov logic networks. 20th National Conference on Artificial Intelligence (pp. 868-873). AAAI Press. Sodiya, A. S. (2005). Data Mining-based Intelligent Equipment Maintenance. Journal of Applied Computer Science, 13, 29-38. Stefanowski, J., & Wilk, S. (2006). Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers. Fundamenta Informaticae, 379-391. Stone, C.J. (1977). Consistent nonparametric regression. Annals of Statistics, 595–645. Swets, J. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285-1293. Tadeusz Pietraszek, A.T. (2005). Data Mining and Machine Learning-Towards Reducing False Positives in Intrusion Detection. Information Security Technical Report Journal, 10, 169-183. Tatavarty, G. (2006). Finding Temporal Association Rules between frequent patterns in Multivariate time series. Master of Computer Science Thesis. University of Cincinnati. Tavenard, R., Salah, A., & Pauwels, E. (2007). Constructing Ambient Intelligence– AmI. In M. Mühlhäuser, A. Ferscha, & E. Aitenbichler, AmI 2007 Workshops Darmstadt, Series: Communications in Computer and Information Science, Vol. 11 (Vol. 11, pp. 53-62). Springer Berlin Heidelberg. Teknomo, K. (2006, Feb 05). Chebyshev Distance. Retrieved Feb 10, 2010, from people.revoledu.com: http://people.revoledu.com/kardi/tutorial/Similarity/Cheby shevDistance.html Tomek, I. (1976). Two modifications of cnn. IEEE Transactions on Systems, Man, 7(2), 679–772. Tse, P.W. (2002). Maintenance practices in Hong Kong and the use of the intelligent scheduler. Journal of Quality in Maintenance Engineering, 8, 369-380. Tse, P., & Li, L. (2001). Intelligent Predictive Decision Support System for Condition-Based Maintenance. The International Journal of Advanced Manufacturing Technology, Volume 17, 383-391. Tzanis, G., Kavakiotis, I., & Vlahavas, I. (2009). Innovations in Database Technologies and Applications: Current and Future Trends. In Handbook of Research on (pp. 1-1124 pp). IGI Global. Vapnik, V. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988-999. Waaki, H. (2002). Error Defect Library. Fuji Electric Malaysia. Wang, D.D., Yang, D., Xu, J., & Xu, K. (1996).Computational intelligence based machine fault diagnosis. Proceedings of the IEEE International Conference on Industrial Technology , 1996. (ICIT ’96) (pp. 465-469). Wang, W. (2008). Some fundamental issues in ensemble methods. Proceedings of the International Joint Conference on Neural Networks, IJCNN (pp. 2243-2250). Hong Kong, China: IEEE. Wang, W., Jones, P., & Partridge, D. (2000). Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems. Multiple Classifier Systems in Lecture Notes in Computer Science series, 1857, pp. 240-249. Wang, W., Partridge, D., & Etherington, J. (2001 ). Hybrid ensembles and coincident-failure diversity. International Joint Conference on Neural Networks (pp. 2376–2381 vol.4). Washington, DC, USA: IEEE. Watanabe, M. (2002). GHT Defect Mode. Fuji Electric Malaysia. Webb, B. (1995). Inductive Learning. Retrieved Feb 12, 2010, from sifter.org: http://sifter.org/~brandyn/Inductive Learning.html Weiss, G.M. (2003, May). The Effect Of Small Disjuncts And Class Distribution On Decision Tree Learning. Doctorate Dissertation. New Brunswick, New Jersey: Rutgers University. Weiss, G.M., & Hirsh, H. (1998). Learning to Predict Rare Events in Categorical Time-Series Data. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (pp. 359-363). Menlo Park,CA: AAAI Press. Weiss, G.M., & Provost, F. (2003). Learning when training data are costly: the effect of class distribution on tree induction. J. Artif. Int. Res., 19 (1), 315-354. Weiss, G.M., McCarthy, K., & Zabar, B. (2007).Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs? In Proceedings of the 2007 International Conference on Data Mining (pp. 35-41). CSREA Press. Weiss, S.M., & Kulikowski, C.A. (1991). Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishers Inc. Witten, I.H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Morgan Kaufmann. Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5, 241-259. Wu, G., & Chang, E.Y. (2003). Class-boundary alignment for imbalanced dataset learning. In ICML 2003 Workshop on Learning from Imbalanced Datasets, (pp. 49-56). Yam, R.C., Tse, P.W., Li, L., & Tu, P. (2001). Intelligent Predictive Decision Support System for Condition-Based Maintenance. The International Journal of Advanced Manufacturing Technology, 17(5), 383-391. Yan, R., Liu, Y., Jin, R., & Hauptmann, A. (2003). On Predicting Rare Classes With Svm Ensembles In Scene Classification. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '03) (pp. 21-24). IEEE. Yang, Z., Djurdjanovic, D., & Ni, J. (2007). Maintenance scheduling in manufacturing systems based on predicted machine degradation. Journal of Intelligent Manufacturing, 19, 87-98. Zeng, Y., Jiang, W., Zhu, C., Liu, J., Teng, W., & Zhang,Y. (2006). Prediction of Equipment Maintenance Using Optimized Support Vector Machine. In D.-S. a. Huang, Computational Intelligence, Lecture Notes in Computer Science series (Vol. Volume 4114, pp. 570-579). Springer Berlin/Heidelberg. Zhang, G., Lee, S., Propes, N., Zhao, Y., Vachtsevanos, G., Thakker, A., et al. (2002). A Novel Architecture for an Integrated Fault Diagnostic/Prognostic System. AAAI symposium. Zhou, J., Li, X., Andernroomer, A.J., Zeng, H., Goh, K.M., Wong, Y.S., et al. (2005).Intelligent Prediction Monitoring System for Predictive Maintenance in manufacturing. 31st Annual Conference of IEEE in Industrial Electronics Society (p. 6 pp). IEEE. Zhou, Z.H. (2003). Book review: Three perspectives of data mining. Artif. Intell., 143(1), 139-146. Zhou, Z.-H., Wu, J., Tang, W., Zhou, Z.-h., Wu, J., & Tang, W. (2002). Ensembling Neural Networks: Many Could Be Better Than All. Artif. Intell., 239-263.

Similar Items