Domain-independent feature extraction for multi-classification using multi-objective genetic programming |
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Authors: | Yang Zhang Peter I Rockett |
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Affiliation: | (1) Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK |
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Abstract: | We propose three model-free feature extraction approaches for solving the multiple class classification problem; we use multi-objective
genetic programming (MOGP) to derive (near-)optimal feature extraction stages as a precursor to classification with a simple
and fast-to-train classifier. Statistically-founded comparisons are made between our three proposed approaches and seven conventional
classifiers over seven datasets from the UCI Machine Learning database. We also make comparisons with other reported evolutionary
computation techniques. On almost all the benchmark datasets, the MOGP approaches give better or identical performance to
the best of the conventional methods. Of our proposed MOGP-based algorithms, we conclude that hierarchical feature extraction
performs best on multi-classification problems. |
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Keywords: | |
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