Abstract
In order to improve the feature selection stability based on evolutionary algorithms, an evolutionary algorithms’ feature selection stability improvement system is proposed. Three Filter methods’ results are aggregated to provide the stability information, and feature selection stability and classification accuracy are adopted as two optimization objectives. Weighted sum, weighted product and biobjective optimization methods together are applied as the system’s optimization models. Ant colony optimization, particle swarm optimization and genetic algorithm are used as testing algorithms, and experiments are taken on two benchmark datasets. The results show that the proposed system can improve the stability of evolutionary algorithms’ feature selection efficiently and their classification performance simultaneously.
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This work was supported by the Natural Science Foundation of China under Grant 61371196.
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Liu, Y., Diao, X., Cao, J., Zhang, L. (2017). Evolutionary Algorithms’ Feature Selection Stability Improvement System. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_6
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DOI: https://doi.org/10.1007/978-981-10-7179-9_6
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