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- W2897100032 abstract "With the advent of the Big Data era, large amount of high-dimensional data is generated, especially in the field of bioinformatics. In these data sets, they often contain many irrelevant, redundant and noisy features. Feature selection can reduce dimensionality and curtail the computational complexity during the analysis of classification problems. In this paper, we propose an improved binary quantum particle swarm optimization (iBQPSO) algorithm for feature selection. Firstly, the maximal information coefficient (MIC) is used to compute the correlation between feature and class label. Then, weak correlation features are removed. The optimal feature subset is selected by iBQPSO algorithm. Finally, the perfermance of our algorithm is measured by the accuracy of classification for the optimal feature subset. The experiments prove that the iBQPSO algorithm for feature selection can get better classification accuracy." @default.
- W2897100032 created "2018-10-26" @default.
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- W2897100032 date "2018-07-01" @default.
- W2897100032 modified "2023-09-28" @default.
- W2897100032 title "iBQPSO: an Improved BQPSO Algorithm for Feature Selection" @default.
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- W2897100032 doi "https://doi.org/10.1109/ijcnn.2018.8489676" @default.
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