智能系统学报2017，Vol.12Issue(5)：595-600,6.DOI:10.11992/tis.201609020
基于高维k-近邻互信息的特征选择方法
Feature selection method based on high dimensional k-nearest neighbors mutual information
摘要
Abstract
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.关键词
特征选择/互信息/k-近邻/高维互信息/多层感知器Key words
feature selection/mutual information/k-nearest neighbor/high-dimensional mutual information/multilayer perceptron分类
信息技术与安全科学引用本文复制引用
周红标,乔俊飞..基于高维k-近邻互信息的特征选择方法[J].智能系统学报,2017,12(5):595-600,6.基金项目
国家自然科学基金重点项目(61533002) （61533002）
国家杰出青年科学基金项目(61225016). （61225016）
