Feature selection algorithm based on Relief and mutual information

Abstract

The invention provides a feature selection algorithm based on Relief and mutual information, and belongs to the field of computer algorithms. The algorithm comprises the following steps of (1) settingan optimal feature subset as an empty set, and setting a weight of the optimal feature subset; (2) selecting features not belonging to the optimal feature subset in all features in one piece of data,putting the features into a candidate optimal feature subset, and calculating a weight of the current candidate optimal feature subset through a composite feature evaluation criterion; (3) evaluatingand replacing the weight of the candidate optimal feature subset at the moment; (4) removing to-be-selected features which do not meet the requirements; and (5) if the to-be-selected features still exist, returning to the step (2) for continuous calculation, otherwise, ending the algorithm. A method provided by the invention is improved for the problem that a Relief feature selection algorithm only can handle the binary classification problem and cannot process redundant features, and an improved Relief weight-based feature selection algorithm is provided, so that the feature selection algorithm has higher calculation accuracy while having high calculation efficiency.

Description

technical field [0001] The invention relates to an improved method of a feature selection algorithm based on Relief and mutual information, belonging to the field of computer algorithms. Background technique [0002] Feature selection algorithms are mainly divided into Filter class, Wrappers class, Embedded class and Hybrid class. Among them, because the feature selection of the Filter class is computationally efficient, it is widely used. Among them, the most representative algorithm in the Filter class is the Relief feature selection algorithm, which is simple in thought and efficient in calculation. However, because it can only deal with binary classification problems, it is restricted in application, and the algorithm cannot deal with the problem of redundant features. The feature selection algorithm in the Hybrid class combines the advantages of this aspect, so it is also widely used. Among them, in the feature selection algorithm, the algorithm that uses mutual info...

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Problems solved by technology

[0003] The present invention provides a feature selection algorithm based on Relief and mutual information, the purpose of which is to solve the problem that the Relief algorithm can only handle binary classification in feature selection, and the algorithm using mutual information as an evaluation criterion has high computational complexity in feature selection. Two problems, the proposed feature selection algorithm based on Relief and mutual information

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