Evaluation of Search Result of Document Search Based GA (DSEGA)

Since the 1940s the problem of information storage and retrieval has attracted increasing attention. Retrieve document becoming ever more difficult. With the advent of computers, a great deal of thought has been given to using them to provide rapid and intelligent retrieval systems. Although i ha...

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Main Author:	Kamal Norfarid, Kamaruddin
Format:	Thesis
Language:	eng eng
Published:	2004
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/1237/1/KAMAL_NORFARID_KAMARUDIN.pdf https://etd.uum.edu.my/1237/2/1.KAMAL_NORFARID_KAMARUDIN.pdf
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id	my-uum-etd.1237
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Kamal Norfarid, Kamaruddin Evaluation of Search Result of Document Search Based GA (DSEGA)
description	Since the 1940s the problem of information storage and retrieval has attracted increasing attention. Retrieve document becoming ever more difficult. With the advent of computers, a great deal of thought has been given to using them to provide rapid and intelligent retrieval systems. Although i has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Genetic algorithms describe a set of optimization techniques that given a goal or fitness function are used to search a space for optimal points. The space in searched in a directed, stochastic manner, and the method of searching borrows some ideas from evolution. In practice, genetic algorithms have proven very effective in searching through complex, highly nonlinear, multidimensional search spaces. DSeGA system is an intelligent search agent toolkit at Faculty of Information Technology of Universiti Utara Malaysia. It is composed by a series of module that using information retrieval method and genetic algorithm. This toolkit does not tested by any standard test data collection. The aim of this research is to test DSeGA system with three standard data collection (Granfield, CACM and TIME). The finding of research is and evaluation of DSeGA system search result. It was discovered that DSeGA system cannot performed the way that the system should be. The conclusion of this research is DSeGA system need to be investigated to enhance the system performance.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Kamal Norfarid, Kamaruddin
author_facet	Kamal Norfarid, Kamaruddin
author_sort	Kamal Norfarid, Kamaruddin
title	Evaluation of Search Result of Document Search Based GA (DSEGA)
title_short	Evaluation of Search Result of Document Search Based GA (DSEGA)
title_full	Evaluation of Search Result of Document Search Based GA (DSEGA)
title_fullStr	Evaluation of Search Result of Document Search Based GA (DSEGA)
title_full_unstemmed	Evaluation of Search Result of Document Search Based GA (DSEGA)
title_sort	evaluation of search result of document search based ga (dsega)
granting_institution	Universiti Utara Malaysia
granting_department	Faculty of Information Technology
publishDate	2004
url	https://etd.uum.edu.my/1237/1/KAMAL_NORFARID_KAMARUDIN.pdf https://etd.uum.edu.my/1237/2/1.KAMAL_NORFARID_KAMARUDIN.pdf
_version_	1747827102466441216
spelling	my-uum-etd.12372013-07-24T12:11:03Z Evaluation of Search Result of Document Search Based GA (DSEGA) 2004 Kamal Norfarid, Kamaruddin Faculty of Information Technology Faculty of Information Technology QA76 Computer software Since the 1940s the problem of information storage and retrieval has attracted increasing attention. Retrieve document becoming ever more difficult. With the advent of computers, a great deal of thought has been given to using them to provide rapid and intelligent retrieval systems. Although i has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Genetic algorithms describe a set of optimization techniques that given a goal or fitness function are used to search a space for optimal points. The space in searched in a directed, stochastic manner, and the method of searching borrows some ideas from evolution. In practice, genetic algorithms have proven very effective in searching through complex, highly nonlinear, multidimensional search spaces. DSeGA system is an intelligent search agent toolkit at Faculty of Information Technology of Universiti Utara Malaysia. It is composed by a series of module that using information retrieval method and genetic algorithm. This toolkit does not tested by any standard test data collection. The aim of this research is to test DSeGA system with three standard data collection (Granfield, CACM and TIME). The finding of research is and evaluation of DSeGA system search result. It was discovered that DSeGA system cannot performed the way that the system should be. The conclusion of this research is DSeGA system need to be investigated to enhance the system performance. 2004 Thesis https://etd.uum.edu.my/1237/ https://etd.uum.edu.my/1237/1/KAMAL_NORFARID_KAMARUDIN.pdf application/pdf eng validuser https://etd.uum.edu.my/1237/2/1.KAMAL_NORFARID_KAMARUDIN.pdf application/pdf eng public masters masters Universiti Utara Malaysia Blair, D.C. and Maron, M.E. (1985). An evaluation of retrieval effectiveness for a full text document retrieval systems. Communications of ACM, 28(3):289-299. Chen, H., Chung, Y., Ramsey, M. & Yang, C.C. 1998(a). A Smart itsy Bitsy Spider for the Web. JASIS 49(7): 604-618 Chen, H., Shankaranarayanan, G., She, L. & Iyer, A. 1998(b). A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing. JASIS 49(8): 693-705. Chen, H. and Dhar, V., (1991). Cognitive process as a basis for intelligent retrieval systems design. Information Processing Management, 27(5): 405-432. Chen, H., Fan, H., Chau, M., and Zeng, D.,(2001), MetaSpider: Meta-Searching and Categorization on the Web. Journal of the American Society of Information Science D.E. Goldberg (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addision-Wesley, Reading, MA. Du, H and Crestani, H. (2002). Retrieval Effectiveness of Written and Spoken Queries: An Experiment Evaluation, University of Strachclyde, UK. Fan, W., Gordon, M. & Pathak, P. (2000). Personalization of search engine services for effective retrieval and knowledge management. ICIS, hlm. 20-34 Gordon, M., (1988). Probabilistic and genetic algorithms for document retrieval. Communication of the ACM, 3l(l0): 1208-1218. Le Roy et.al. (2003). The Used of Dynamic Context to Improve Casual Internet Search. Journal of the ACM,21 (3):229-253. Martin-Bautista, M.J., Vila, M. & Larsen, H.L. (1999). A Fuzzy Genetic Algorithm Approach to an Adaptive Information R.etrieva1 Agent. Journal of the American Society for Information Science. 50(9):760-771 Nick, 2.Z and Themis, P. (2001). Web Search Using Genetic Algorithm. IEEE Internet Computing, 2001. Oren, R., (2002), Reexamining tf.idf based information retrieval with Genetic Programming, Proc. of SAICSIT: 224-234. Oren, R., (2000), Improving the Effectiveness of information Retrieval with Genetic Programming, Thesis Report. Quinlan, J.R. (1979). Discovering rules by induction from large collections of examples. In Expert System in the Micro-electronic Age, p168-201, Michie,D., Editor, Edinburgh univ. Press. Rumelhart, D.E. Hinton, G.E. and J.Williams (1986). Learning internal representations by error propagation. In Parallel Distributed Processing, p318-362, MIT Press, Cambridge, MA. Sarwar et.al (2000). Analysis of Recommendation Algorithms for E-Commerce Journal of the ACM, 17-20. Simon, H., (1991). Artificial Intelligence : where has it been, and where is it going? IEEE Transactions on Knowledge and Data Engineering, 3(2):128-136. Shamrie Sainin (1999). Agen Carian: Carian Dokumen Berasakan Genetik Algoritma, Laporan Projek Latihan Industri, Universiti Utara Malaysia Weiss, S.M. and Kulikowski, C.A. (1991). Computer Systems That Learn : Classification and Prediction Method from Statistics, Neural Networks, Machine Learning and Expert Systems. Morgan Kaufman publishers, Inc.,San Mateo, CA. Yang, J. & Korfhage, R.R. (1993). Effects of query term weights modification in document retrieval: a study based on a genetic algorithm. Proceedings of the Second Annual Symposium on Document Analysis and Information Retrieval, Las Vegas. hlm 271-285. P. J.M. van Laarhoven and E.H.L. Aarts (1988). Simulated Annealing : Theory and Applications. D. Reidal Publishing Company, Dordrecht. Wu, J.L. and Agogino, A.M, (2004), Automatic Key Phrase Extraction with Multi-Objectives Genetic Algorithm, Proc. of Hawai International of Science,2004. William R. Hersh, Diane L. Elliot, David H. Hickam, Stephanie L. Wolf, and Anna Molnar (1995). Towards new measures of information retrieval evaluation. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 164-170 Yuri, K. and Jochen, R.M, (2003), Current Status of Evaluation of Information Retrieval, Journal of Medical System, 27(5).

Evaluation of Search Result of Document Search Based GA (DSEGA)

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