Genetic Algorithm Based on Attribute Correlation for Multi-label Classification

Abstract

The classifier chains (CC) model has been used widely for multi-label classification, its remarkable characteristic is in consideration of the association between the labels, and the CC method adds the classifiers before it to predict the current instance. Then, the association between the labels is added to each of the current classification of the instance. However, because the CC model requires all the labels to join the chain, the disadvantage of the CC model is that the labels with wrong or redundant information will affect the performance of the classifier. Considering the issue, this paper proposes a genetic algorithm (GA) based on attribute correlation for multi-label classification. The results of the experiments prove that the performance of classification can be improved.