Investigation of improved evolutionary feature selection techniques for biomedical applications

In the hypothesis of pattern recognition, the classification of medical datasets is becoming a challenging task due to the large number of features and training data limitations. Redundancies present in these irrelevant features affect the overall classification accuracy. Such insurmountable issu...

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Main Author:	Sindhu, Ravindran
Format:	Thesis
Language:	English
Subjects:	Feature Selection (FS) Genetic Algorithm (GA) Biomedical applications Medical datasets Improvement
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/2/Full%20text.pdf
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spelling	my-unimap-608442019-06-26T08:21:36Z Investigation of improved evolutionary feature selection techniques for biomedical applications Sindhu, Ravindran Dr. Asral Bahari Jambek In the hypothesis of pattern recognition, the classification of medical datasets is becoming a challenging task due to the large number of features and training data limitations. Redundancies present in these irrelevant features affect the overall classification accuracy. Such insurmountable issues to classification can be overcome by Feature Selection (FS) techniques performed through combinatorial optimization methods such as Genetic Algorithm (GA). However, there are many limitations in basic GA like premature convergence, low degree of solution accuracy due to fixed crossover and mutation rates, low convergence capacity and lack of population diversity. These limitations restrict the genetic search from reaching global optimal solution and thereby reducing their efficiency as a FS method. To overcome these issues, this thesis suggests an effective wrapper framework with some acceptable solutions i.e. firstly, by adding fitness scaling technique (called sigma scaling) along with the Stochastic Universal Sampling (SUS) selection function. This scaling technique has enabled the genetic search to re-adjust the fitness values of the population, which has prevented GA from reaching premature convergence. Secondly, the degree of solution accuracy is improved by adaptively changing the crossover and mutation rates based on the population fitness. Further, the masking concepts of crossover and mutation enhanced the population diversity and increased the convergence capacity as well. To measure the quality of the chromosomes, three different objective functions namely Classification Accuracy, Geometric Mean and a weighted aggregation of Geometric mean and sum of selected features have been employed. The classifiers employed along with ten-fold cross validation method have served as an evaluator of the proposed Improved Genetic Algorithm (IGA) algorithm. The experimental results are presented in terms of several performance measures like Positive prediction, Negative prediction, Sensitivity, Specificity, Accuracy, F-measure, AUC, Kappa statistic and G-mean. Through the proposed IGA method, promising classification accuracy has been obtained for all the six benchmark datasets i.e. 100% for MEEI dataset, 99.49% for PD dataset, 84% for CAD dataset, 99.24% for ES dataset, 94.34% for BT dataset & 94% for CTG dataset. Also, a reduced feature subset has been attained for all these datasets as well i.e. 8 features for MEEI & PD dataset, 3 out of 9 features for CAD dataset, 14 out of 34 features for ES dataset, 3 out of 13 features for BT dataset and 6 out of 22 features for CTG dataset has been obtained espectively. On the whole, experimental results show that the proposed algorithm has evolved a feature subset with a smaller number of features and higher classification performance than using all the features. All the algorithms used in these experiments were simulated using MATLAB 2011a. Universiti Malaysia Perlis (UniMAP) 2014 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/60844 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/1/Page%201-24.pdf f6767295a6d34c40a9e40a399cc2e3cc http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/2/Full%20text.pdf c8c3ffab3c74a0b3bcba75ea02763c2f http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Feature Selection (FS) Genetic Algorithm (GA) Biomedical applications Medical datasets Improvement School of Microelectronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Asral Bahari Jambek
topic	Feature Selection (FS) Genetic Algorithm (GA) Biomedical applications Medical datasets Improvement
spellingShingle	Feature Selection (FS) Genetic Algorithm (GA) Biomedical applications Medical datasets Improvement Sindhu, Ravindran Investigation of improved evolutionary feature selection techniques for biomedical applications
description	In the hypothesis of pattern recognition, the classification of medical datasets is becoming a challenging task due to the large number of features and training data limitations. Redundancies present in these irrelevant features affect the overall classification accuracy. Such insurmountable issues to classification can be overcome by Feature Selection (FS) techniques performed through combinatorial optimization methods such as Genetic Algorithm (GA). However, there are many limitations in basic GA like premature convergence, low degree of solution accuracy due to fixed crossover and mutation rates, low convergence capacity and lack of population diversity. These limitations restrict the genetic search from reaching global optimal solution and thereby reducing their efficiency as a FS method. To overcome these issues, this thesis suggests an effective wrapper framework with some acceptable solutions i.e. firstly, by adding fitness scaling technique (called sigma scaling) along with the Stochastic Universal Sampling (SUS) selection function. This scaling technique has enabled the genetic search to re-adjust the fitness values of the population, which has prevented GA from reaching premature convergence. Secondly, the degree of solution accuracy is improved by adaptively changing the crossover and mutation rates based on the population fitness. Further, the masking concepts of crossover and mutation enhanced the population diversity and increased the convergence capacity as well. To measure the quality of the chromosomes, three different objective functions namely Classification Accuracy, Geometric Mean and a weighted aggregation of Geometric mean and sum of selected features have been employed. The classifiers employed along with ten-fold cross validation method have served as an evaluator of the proposed Improved Genetic Algorithm (IGA) algorithm. The experimental results are presented in terms of several performance measures like Positive prediction, Negative prediction, Sensitivity, Specificity, Accuracy, F-measure, AUC, Kappa statistic and G-mean. Through the proposed IGA method, promising classification accuracy has been obtained for all the six benchmark datasets i.e. 100% for MEEI dataset, 99.49% for PD dataset, 84% for CAD dataset, 99.24% for ES dataset, 94.34% for BT dataset & 94% for CTG dataset. Also, a reduced feature subset has been attained for all these datasets as well i.e. 8 features for MEEI & PD dataset, 3 out of 9 features for CAD dataset, 14 out of 34 features for ES dataset, 3 out of 13 features for BT dataset and 6 out of 22 features for CTG dataset has been obtained espectively. On the whole, experimental results show that the proposed algorithm has evolved a feature subset with a smaller number of features and higher classification performance than using all the features. All the algorithms used in these experiments were simulated using MATLAB 2011a.
format	Thesis
author	Sindhu, Ravindran
author_facet	Sindhu, Ravindran
author_sort	Sindhu, Ravindran
title	Investigation of improved evolutionary feature selection techniques for biomedical applications
title_short	Investigation of improved evolutionary feature selection techniques for biomedical applications
title_full	Investigation of improved evolutionary feature selection techniques for biomedical applications
title_fullStr	Investigation of improved evolutionary feature selection techniques for biomedical applications
title_full_unstemmed	Investigation of improved evolutionary feature selection techniques for biomedical applications
title_sort	investigation of improved evolutionary feature selection techniques for biomedical applications
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Microelectronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/60844/2/Full%20text.pdf
_version_	1747836840425029632

Investigation of improved evolutionary feature selection techniques for biomedical applications

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