Feature selection method based on attribute condition redundancy

The invention discloses a feature selection method based on attribute condition redundancy. The method comprises the following steps: step 1, preprocessing a data set; 2, dividing the preprocessed data set into a training data set and a test data set; 3, calculating a mutual information value between each target feature in the training data set and a class label, selecting the target feature which enables the current mutual information value to be maximum, deleting the target feature from the original data set, adding the target feature into a set S which is initially empty, and then iteratively performing feature selection according to a feature selection algorithm based on attribute condition redundancy; and adding the features selected by each iteration into the set S, and carrying out iteration to finally obtain a feature subset with the size of m. According to the method, redundant information of two measurement features, namely redundancy and attribute condition redundancy, is used, so that some redunda