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• W2557392950 abstract "Recent research has shown that Particle Swarm Optimisation is a promising approach to feature selection. However, applying it on high-dimensional data with thousands to tens of thousands of features is still challenging because of the large search space. While filter approaches are time efficient and scalable for high-dimensional data, they usually obtain lower classification accuracy than wrapper approaches. On the other hand, wrapper methods require a longer running time than filter methods due to the learning algorithm involved in fitness evaluation. This paper proposes a new strategy of combining filter and wrapper approaches in a single evolutionary process in order to achieve smaller feature subsets with better classification performance in a shorter time. A new local search heuristic using symmetric uncertainty is proposed to refine the solutions found by PSO and a new hybrid fitness function is used to better evaluate candidate solutions. The proposed method is examined and compared with three recent PSO based methods on eight high-dimensional problems of varying difficulty. The results show that the new hybrid PSO is more effective and efficient than the other methods." @default.
• W2557392950 created "2016-12-08" @default.
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• W2557392950 date "2016-07-01" @default.
• W2557392950 modified "2023-10-18" @default.
• W2557392950 title "A PSO based hybrid feature selection algorithm for high-dimensional classification" @default.
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• W2557392950 doi "https://doi.org/10.1109/cec.2016.7744271" @default.
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