Multi-class Semi-supervised Logistic I-RELIEF Feature Selection Based on Nearest Neighbor
The multi-class semi-supervised logistic I-RELEIF (MSLIR) algorithm has been proposed and showed its feature selection ability using both labeled and unlabeled samples. Unfortunately, MSLIR is poor when predicting labels for unlabeled samples. To solve this issue, this paper presents a novel multi-class semi-supervised logistic I-RELEIF based on nearest neighbor (MSLIR-NN) for multi-class feature selection tasks. To generate better margin vectors for unlabeled samples, MSLIR-NN uses the nearest neighbor scheme to first predict the labels of unlabeled samples and then calculates their margin vectors according to these estimated labels. Experimental results demonstrate that MSLIR-NN can improve the prediction accuracy of unlabeled data.
KeywordsLogistic I-RELIEF Feature selection Multi-class classification Semi-supervised Nearest neighbor
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373093, by the Soochow Scholar Project of Soochow University, and by the Six Talent Peak Project of Jiangsu Province of China.
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