Interactive evolutionary approaches to multi-objective feature selection

Date

2016

Author

Özmen, Müberra

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In feature selection problems, the aim is to select a subset of features to characterize an output of interest. In characterizing an output, we may want to consider multiple objectives such as maximizing classification performance, minimizing number of selected features or cost, etc. We develop a preference-based approach for multi-objective feature selection problems. Finding all Pareto optimal subsets may turn out to be a computationally demanding problem and we still would need to select a solution eventually. Therefore, we develop interactive evolutionary approaches that aim to converge to a subset that is highly preferred by the decision maker. We test our approach on several instances simulating decision-maker preferences by underlying preference functions and demonstrate that it works well.

Subject Keywords

Machine learning., Time-series analysis., Computer algorithms., Evolutionary computation.

URI

http://etd.lib.metu.edu.tr/upload/12620291/index.pdf
https://hdl.handle.net/11511/25901

Collections

Graduate School of Natural and Applied Sciences, Thesis