Hybrid Multistage Feature Selection Method and its Application in Chinese Medicine

Ming Liu, Jianqiang Du, Zhiqing Li, Jigen Luo, Bin Nie, Mengting Zhang
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Abstract

The experimental data on traditional Chinese medicine efficacy has many irrelevant and redundant features, and different feature combinations have different effects. Therefore, we propose a hybrid multistage feature selection algorithm based on approximate Markov blanket and improved black widow algorithm. The first stage remove irrelevant features by the maximum information coefficient. The second stage delete redundant features from clustered searched by approximate Markov blanket by Lasso algorithm to avoid information loss. The third stage search the optimal feature subset by improved black widow algorithm that used the fast reproduction strategy, the child eating mother strategy and the population restriction strategy. The proposed approach is tested on the basic material data of traditional Chinese medicine and 9 UCI datasets, and compared with other feature selection algorithms. The experimental results show that the algorithm can obtain a small number of feature subsets with high accuracy, and has good time performance.
混合多阶段特征选择方法及其在中医中的应用
中药疗效实验数据存在许多不相关、冗余的特征,不同的特征组合效果不同。为此,我们提出了一种基于近似马尔可夫毯子和改进黑寡妇算法的混合多阶段特征选择算法。第一阶段通过最大信息系数去除不相关特征。第二阶段,利用Lasso算法从聚类搜索的近似马尔可夫毯中删除冗余特征,避免信息丢失;第三阶段采用改进的黑寡妇算法,结合快速繁殖策略、子吃母策略和种群限制策略,搜索最优特征子集。在中药基础材料数据和9个UCI数据集上对该方法进行了测试,并与其他特征选择算法进行了比较。实验结果表明,该算法能够以较高的准确率获得数量较少的特征子集,并具有良好的时效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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