Abstract
Feature selection (FS) has been studied as a multi-objective problem in recent years to improve the classification effect. In this paper, to expand the ability of Bacterial Foraging Optimization (BFO) in feature selection problems for classification, a Multi-objective Adapting Chemotaxis Bacterial Foraging Optimization (abbreviated as MOACBFO) is proposed. In MOACBFO, a structural variation strategic model is proposed to reduce the computational complexity for multi-objective problems and applied with a dynamically updated external matrix to record the performance of bacteria on two objectives. In addition, an adaptive chemotaxis step mechanism is designed to help the bacteria jump out of the local optimality. To further enhance the diversity of bacteria searching capability, a feature subset updating strategy is developed. The optimal feature subset and fitness value are stored in the external matrix continuously by comparing them with the historical value records. The performance of the proposed algorithm is demonstrated by comparing it with four other advanced swarm intelligent algorithms on 11 high-dimensional microarray datasets. The results indicate that the MOACBFO performs better in achieving a lower classification error rate using a small number of features.
This paper is submitted to Special Session on Intelligent Data Mining: Techniques and Applications (Session Chair: Ben Niu, Hong Wang)
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Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71901152), Natural Science Foundation of Guangdong Province (2020A1515010752), and Natural Science Foundation of Shenzhen University (85303/00000155).
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Wang, H., Ou, Y., Wang, Y. (2021). A Multi-objective Structure Variant Bacterial Heuristic Feature Selection Method in High-dimensional Data Classification. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_34
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