FEATURE SELECTION USING MULTI OBJECTIVE GENETIC ALGORITHM WITH SUPPORT VECTOR MACHINE

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ELIKAEI AHARI MOJGAN; NASERSHARIF BABAK

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTER AND ROBOTICS Year:2016 | Volume:9 | Issue:2 Page(s): 11-17

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Information Journal Paper

Title

FEATURE SELECTION USING MULTI OBJECTIVE GENETIC ALGORITHM WITH SUPPORT VECTOR MACHINE

Author(s)

ELIKAEI AHARI MOJGAN | NASERSHARIF BABAK | Issue Writer Certificate

Keywords

FEATURE SELECTION

MULTI OBJECTIVE GENETIC ALGORITHM

SUPPORT VECTOR MACHINE

Abstract

Different approaches have been proposed for FEATURE SELECTION to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measures like inter-class distance, features statistical independence or information theoretic measures. Even though, wrapper methods use a classifier to evaluate features subsets by their predictive accuracy (on test data) by statistical resampling or cross-validation. Filter methods usually use only one measure for FEATURE SELECTION that does not necessarily produce the best result. In this paper, we proposed to use the classification error measures besides to filter measures where our classifier is SUPPORT VECTOR MACHINE (SVM). To this end, we use MULTI OBJECTIVE GENETIC ALGORITHM. In this way, one of our FEATURE SELECTION measure is SVM classification error. Another measure is selected between mutual information and Laplacian criteria which indicates informative content and structure preserving property of features, respectively. The evaluation results on the UCI datasets show the efficiency of this method.

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References

FEATURE SELECTION BASED ON HYBRIDIZATION OF GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

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APA: Copy

ELIKAEI AHARI, MOJGAN, & NASERSHARIF, BABAK. (2016). FEATURE SELECTION USING MULTI OBJECTIVE GENETIC ALGORITHM WITH SUPPORT VECTOR MACHINE. JOURNAL OF COMPUTER AND ROBOTICS, 9(2), 11-17. SID. https://sid.ir/paper/689270/en

Vancouver: Copy

ELIKAEI AHARI MOJGAN, NASERSHARIF BABAK. FEATURE SELECTION USING MULTI OBJECTIVE GENETIC ALGORITHM WITH SUPPORT VECTOR MACHINE. JOURNAL OF COMPUTER AND ROBOTICS[Internet]. 2016;9(2):11-17. Available from: https://sid.ir/paper/689270/en

IEEE: Copy

MOJGAN ELIKAEI AHARI, and BABAK NASERSHARIF, “FEATURE SELECTION USING MULTI OBJECTIVE GENETIC ALGORITHM WITH SUPPORT VECTOR MACHINE,” JOURNAL OF COMPUTER AND ROBOTICS, vol. 9, no. 2, pp. 11–17, 2016, [Online]. Available: https://sid.ir/paper/689270/en

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