An ensemble learning algorithm for feature selection based on solution to multi-class imbalance data classification
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Abstract:
In order to solve the problem of unbalanced multi-classification, a feature selection and AdaBoost integration method is proposed. First, the data is preprocessed. The WSPSO algorithm is used to select features, and the initial population is constructed according to the importance of the feature. The initial algorithm can be carried out along the correct search direction to reduce the influence of incoherent features. Secondly, the AdaBoost algorithm is more sensitive to sample weights, and the attention to small samples is enhanced. And using AUCare is used, as the evaluation standard, because compared with other evaluation criteria, AUCare has the advantage of visualization and is more sensitive to poor AUC. Finally, compared with several other unbalanced classification algorithms on the unbalanced data set, the algorithm can effectively deal with the unbalanced multi-classification problem.
