WXGCB: A Clustering Prior Weighting Semi-Supervised Learning Method Based on Space Level Constraint and Mixed Variable Metrics

Abstract

A clustering prior weighted semi-supervised learning method called WXGCB has been proposed, which combines the characteristics of the cluster-then-label semi-supervised method and space-level constraint semi-supervised method. WXGCB can use mixed variable information, data prior information, and clustering prior information based on different clustering algorithms to adjust the distance matrix, thereby transforming different supervised learning algorithms into semi-supervised learning algorithms for improving their prediction accuracy. Due to the fact that WXGCB does not require internal adjustments to the clustering algorithms and supervised learning algorithms used, this method can flexibly combine different clustering algorithms and supervised learning algorithms to find combinations that can better compensate for each other's shortcomings, and can easily convert various supervised learning algorithms into semi-supervised learning algorithms. To verify the effectiveness of WXGCB, WXGCB transformed two supervised learning algorithms KSNN and DBGLM into semi-supervised mixed variable learning algorithms SMKSNN and SMGLM, and conducted performance comparison experiments with the other two semi-supervised learning algorithms on six benchmark datasets.