CostSensitive Feature Selection Based on Label Significance and Positive Region Costsensitive feature selection is an important research topic in the field of machine learning and data mining. Presently, costsensitive feature selection research is mainly oriented to singlelabel or multilabel data. Since in many fields of application, there is a correlation and significance among the labels for multilabel data. In order to resolve the problems, this paper introduces label significance into costsensitive feature selection, and proposes a feature selection algorithm using test cost based on label significance. The algorithm combines the test cost matrix generated by the three distributions with positive region. Finally, the effectiveness and feasibility of the algorithm are further verified by experimental results on the four Mulan data set.

Optimal Feature Aggregation and Combination for TwoDimensional Ensemble Feature Selection Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5xe2x80x9314% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF.