Date of Award

2010

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Robin Gras

Keywords

Applied sciences

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

The goal of our research is to find how dependencies affect the capability of several feature selection approaches to extract of the relevant features for a classification purpose. A new method using pre-designed Bayesian Networks is proposed to generate the test datasets with an easy tuning level of complexity. Relief, CFS, NB-GA, NB-BOA, SVM-GA, SVM-BOA and SVM-mBOA these feature selection approaches are used and evaluated. The higher level of dependency among the relevant features can affect the capability to find the relevant features for classification. For Relief, SVM-BOA and SVM-mBOA, if the dependencies among the irrelevant features are altered, the performance changes as well. Relief is an efficient method in normal case except some extreme situations. Moreover, a multi-objective optimization method is used to keep the diversity of the populations in each generation of the BOA search algorithm improving the overall quality of solutions in our experiments.

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