Semi-Supervised Self-Training Feature Weighted Clustering Decision Tree and Random Forest

Abstract

A self-training algorithm is an iterative method for semi-supervised learning, which wraps around a base learner. It uses its own predictions to assign labels to unlabeled data. For a self-training algorithm, the classification ability of the base learner and the estimation of prediction confidence are very important. The classical decision tree as the base learner cannot be effective in a self-training algorithm, because it cannot correctly estimate its own predictions. In this paper, we propose a novel method of node split of the decision trees, which uses weighted features to cluster instances. This method is able to combine multiple numerical and categorical features to split nodes. The decision tree and random forest constructed by this method are called FWCDT and FWCRF respectively. FWCDT and FWCRF have the better classification ability than the classical decision trees and forests based on univariate split when the training instances are fewer, therefore, they are more suitable as the base classifiers in self-training. What's more, on the basis of the proposed node-split method, we also respectively explore the suitable prediction confidence measurements for FWCDT and FWCRF. Finally, the results of experiment implemented on the UCI datasets show that the self-training feature weighted clustering decision tree (ST-FWCDT) and random forest (ST-FWCRF) can effectively exploit unlabeled data, and the final obtained classifiers have better generalization ability.

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“…The perpendicular line between the two class centroids The perpendicular line between the two cluster centers en, we use the RELIEF-F algorithm to calculate the weights of four features, which are 0.09, 0.14, 0.34, and 0.39, respectively. In the process of k-means clustering, the distances between instances and cluster centers are calculated by (10), where p indicates the number of features and w l indicates the weight of the lth feature. We obtain 6 misclassified samples, and the specific results are shown in Table 2.…”

“…en remove features whose weights are less than 1/5 of the maximum. Finally, the distance is calculated according to formula (10). Fun2 uses the center of each class as the initial cluster center of k-means and the outputs of k-means as the partition results.…”

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