Entropy-based feature selection for microarray data
Aiming at the problem that the entropy-based feature weighting algorithm ignores the influence of the intrinsic characteristics of the dataset on the importance of features,which leads to poor feature selection,an improved entropy-based feature weighting algorithm is proposed,which calculates the importance weights of feature dimensions according to the information entropy,achieves threshold learning of different datasets by introducing cross-validation,so as to determines the optimal threshold parameter used to measure the importance of the features,and performs feature selection of the dataset based on this threshold.The numerical experimental results on the microarray dataset show that the proposed algorithm is able to reduce more dimensions than the original algorithm,and the accuracy of the feature subset used for classification is basically the same as the original algorithm or even better than that,which indicates that the improved algorithm is feasible and effective.
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