Abstract
The process of feature selection (FS) is a substantial task that has a significant effect in the performance of a given algorithm. The goal is to choose a subset of available features by eliminating the unnecessary features. This hybrid algorithm is in maximising the classification performance and minimising the number of features to achieve an outstanding performance through a less complex procedure. From the experiments, FSMOGSA was noted to be quite unparalleled in comparison with other methods in reducing the error rate, and maximising the general performance through irrelevant feature reduction.
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Acknowledgements
This project is supported by the National Natural Science Foundation of China (61472161, 61133011, 61303132, 61202308), Science & Technology Development Project of Jilin Province (20140101201JC, 201201131).
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Dickson, B.B., Wang, S., Dong, R., Wen, C. (2016). A Feature Selection Method Based on Multi-objective Optimisation with Gravitational Search Algorithm. In: Bian, F., Xie, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49155-3_57
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