[1]ZHOU Hongbiao,QIAO Junfei.Feature selection method based on high dimensional k-nearest neighbors mutual information[J].CAAI Transactions on Intelligent Systems,2017,12(5):595-600.[doi:10.11992/tis.201609020]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
12
Number of periods:
2017 5
Page number:
595-600
Column:
学术论文—人工智能基础
Public date:
2017-10-25
- Title:
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Feature selection method based on high dimensional k-nearest neighbors mutual information
- Author(s):
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ZHOU Hongbiao1; 2; 3; QIAO Junfei1; 2
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1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
3. Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China
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- Keywords:
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feature selection; mutual information; k-nearest neighbor; high-dimensional mutual information; multilayer perceptron
- CLC:
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TP183
- DOI:
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10.11992/tis.201609020
- Abstract:
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Feature selection plays an important role in the modeling and forecast of multivariate series. In this paper, we propose a feature selection method based on data-driven high-dimensional k-nearest neighbor mutual information. First, this method extends the k-nearest neighbor method to estimate the amount of mutual information among high-dimensional feature variables. Next, optimal sorting of all these features is achieved by adopting a forward accumulation strategy in which irrelevant features are eliminated according to a preset number. Then, redundant features are located and removed using a backward cross strategy. Lastly, this method obtains optimal subsets that feature a strong correlation. Using Friedman data, housing data, and actual effluent total-phosphorus forecast data from wastewater treatment plant as examples, we performed a simulation experiment by adopting a neural network forecast model with multilayer perception. The simulation results demonstrate the feasibility of the proposed method.