Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter
This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning...
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| Format: | Article |
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Inderscience Publishers
2017
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| _version_ | 1803628270579613696 |
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| author | Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
| author_facet | Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana |
| author_sort | Alwan, Hiba Basim |
| collection | UUM |
| description | This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM.Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. The objective of this paper is to formulate an algorithm for tuning SVM parameters and feature subset selection.This can be achieved by simultaneously performing the selection of feature subset and tuning SVM parameters tasks. ACOMV-SVM algorithm is able to simultaneously tune SVM parameters and feature subset selection. Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection. |
| first_indexed | 2024-07-04T06:19:17Z |
| format | Article |
| id | uum-21974 |
| institution | Universiti Utara Malaysia |
| last_indexed | 2024-07-04T06:19:17Z |
| publishDate | 2017 |
| publisher | Inderscience Publishers |
| record_format | dspace |
| spelling | uum-219742017-05-07T06:58:47Z https://repo.uum.edu.my/id/eprint/21974/ Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana QA76 Computer software This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine.A new direction for ant colony optimisation is to optimise mixed (discrete and continuous) variables.The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM.Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. The objective of this paper is to formulate an algorithm for tuning SVM parameters and feature subset selection.This can be achieved by simultaneously performing the selection of feature subset and tuning SVM parameters tasks. ACOMV-SVM algorithm is able to simultaneously tune SVM parameters and feature subset selection. Experimental results obtained from the proposed algorithm are better compared with other approaches in terms of classification accuracy and feature subset selection. Inderscience Publishers 2017 Article PeerReviewed Alwan, Hiba Basim and Ku-Mahamud, Ku Ruhana (2017) Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter. International Journal of Bio-Inspired Computation, 9 (1). p. 53. ISSN 1758-0366 http://doi.org/10.1504/IJBIC.2017.081842 doi:10.1504/IJBIC.2017.081842 doi:10.1504/IJBIC.2017.081842 |
| spellingShingle | QA76 Computer software Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title | Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title_full | Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title_fullStr | Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title_full_unstemmed | Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title_short | Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| title_sort | mixed variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter |
| topic | QA76 Computer software |
| work_keys_str_mv | AT alwanhibabasim mixedvariableantcolonyoptimisationalgorithmforfeaturesubsetselectionandtuningsupportvectormachineparameter AT kumahamudkuruhana mixedvariableantcolonyoptimisationalgorithmforfeaturesubsetselectionandtuningsupportvectormachineparameter |