采用动态相关度权重的特征选择算法OA北大核心CSTPCD

When considering the new classification information provided by the candidate features,the features selection algorithm based on mutual information usually ignores that the addition of the candidate features will cause the change of the correlation between the selected features and class labels,which will bring additional new information;in addition,when calculating redundant information,using the form of cumulative summation may lead to the underestimation of the redundancy degree of candidate features.In view of the above problems,the definition of dynamic relevance weight is proposed to more comprehensively consider the new information components brought by candidate features.The im-proved definition of redundant items is proposed,and the maximum and normalization strategy are adopted to solve the problem of underestimating redundancy.On this basis,the feature selection using dynamic relevance weight(FSDRW)is proposed.Five current mainstream filter-based feature selection algorithms based on mutual information are selected for comparative experiments.Experiments on machine learning test datasets from UCI(University of California Irvine)and ASU(Arizona State University)show that the proposed algorithm works well in classification accuracy and comprehen-sive performance.Finally,the proposed algorithm is applied to the recognition of microseismic and blasting signals in a reservoir project in Guangxi.The selected features of the algorithm are used for microseismic signal recognition,achieving a classification accuracy of 98.86%,verifying the effectiveness of the algorithm in practical applications.