Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification
Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are depend...
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my.utm.979282022-11-07T10:48:32Z http://eprints.utm.my/id/eprint/97928/ Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification Tengku Ab. Hamid, Tengku Mazlin Sallehuddin, Roselina Mohd. Yunos, Zuriahati Ali, Aida QA75 Electronic computers. Computer science Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are dependent, conducting them independently could deteriorate the accuracy performance. Filter algorithm is used to eliminate irrelevant features based on ranking. However, independent filter still incapable to consider features dependency and resulting in imbalance selection of significant features which consequently degrade the classification performance. In order to mitigate this problem, ensemble of multi filters algorithm such as Information Gain (IG), Gain Ratio (GR), Chi-squared (CS) and Relief-F (RF) are utilized as it can considers the intercorrelation between features. The proper kernel parameters settings may also influence the classification performance. Hence, a harmonize classification technique using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is employed to optimize the searching of optimal significant features and kernel parameters synchronously without degrading the accuracy. Therefore, an ensemble filter feature selection with harmonize classification of PSO and SVM (Ensemble-PSO-SVM) are proposed in this research. The effectiveness of the proposed method is examined on standard Breast Cancer and Lymphography datasets. Experimental results showed that the proposed method successfully signify the classifier accuracy performance with optimal significant features compared to other existing methods such as PSO-SVM and classical SVM. Hence, the proposed method can be used as an alternative method for determining the optimal solution in handling high dimensional data. Elsevier Ltd 2021-09-15 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/97928/1/RoselinaSallehuddin2021_EnsembleBasedFilterFeatureSelection.pdf Tengku Ab. Hamid, Tengku Mazlin and Sallehuddin, Roselina and Mohd. Yunos, Zuriahati and Ali, Aida (2021) Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification. Machine Learning with Applications, 5 (NA). pp. 1-14. ISSN 2666-8270 http://dx.doi.org/10.1016/j.mlwa.2021.100054 DOI:10.1016/j.mlwa.2021.100054 |
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QA75 Electronic computers. Computer science Tengku Ab. Hamid, Tengku Mazlin Sallehuddin, Roselina Mohd. Yunos, Zuriahati Ali, Aida Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
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Explosive increase of dataset features may intensify the complexity of medical data analysis in deciding necessary treatment for the patient. In most cases, the accuracy of diagnosis system is vitally impacted by the data dimensionality and classifier parameters. Since these two processes are dependent, conducting them independently could deteriorate the accuracy performance. Filter algorithm is used to eliminate irrelevant features based on ranking. However, independent filter still incapable to consider features dependency and resulting in imbalance selection of significant features which consequently degrade the classification performance. In order to mitigate this problem, ensemble of multi filters algorithm such as Information Gain (IG), Gain Ratio (GR), Chi-squared (CS) and Relief-F (RF) are utilized as it can considers the intercorrelation between features. The proper kernel parameters settings may also influence the classification performance. Hence, a harmonize classification technique using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is employed to optimize the searching of optimal significant features and kernel parameters synchronously without degrading the accuracy. Therefore, an ensemble filter feature selection with harmonize classification of PSO and SVM (Ensemble-PSO-SVM) are proposed in this research. The effectiveness of the proposed method is examined on standard Breast Cancer and Lymphography datasets. Experimental results showed that the proposed method successfully signify the classifier accuracy performance with optimal significant features compared to other existing methods such as PSO-SVM and classical SVM. Hence, the proposed method can be used as an alternative method for determining the optimal solution in handling high dimensional data. |
format |
Article |
author |
Tengku Ab. Hamid, Tengku Mazlin Sallehuddin, Roselina Mohd. Yunos, Zuriahati Ali, Aida |
author_facet |
Tengku Ab. Hamid, Tengku Mazlin Sallehuddin, Roselina Mohd. Yunos, Zuriahati Ali, Aida |
author_sort |
Tengku Ab. Hamid, Tengku Mazlin |
title |
Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
title_short |
Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
title_full |
Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
title_fullStr |
Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
title_full_unstemmed |
Ensemble based filter feature selection with harmonize Particle Swarm Optimization and Support Vector Machine for optimal cancer classification |
title_sort |
ensemble based filter feature selection with harmonize particle swarm optimization and support vector machine for optimal cancer classification |
publisher |
Elsevier Ltd |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/97928/1/RoselinaSallehuddin2021_EnsembleBasedFilterFeatureSelection.pdf http://eprints.utm.my/id/eprint/97928/ http://dx.doi.org/10.1016/j.mlwa.2021.100054 |
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1751536123322040320 |
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