Three-way decision-based tri-training with entropy minimization

The three-way decision (TWD) theory is an effective methodology and philosophy for thinking in three and has been successfully applied to knowledge reasoning and decision making. However, limited research has been devoted to learning from partially labeled data with discrete and continuous attributes using TWD. In this study, we propose a TWD-based tri-training model for partially labeled data with heterogeneous attributes. First, a measure of semi-supervised neighborhood mutual information is defined, based on which a heuristic algorithm is developed to generate an optimal semi-supervised reduct of partially labeled data. Then, a tri-training model is trained on the original view along with two views transformed by data discretization and principal component analysis, and the strategy of TWD with entropy minimization is further introduced to classify unla-beled data into useful, uncertain, and useless samples, whereas the multiview tri-training model is iteratively retrained on only a certain number of useful samples with low entropy to improve the performance. Finally, the effectiveness of the proposed model is theoreti-cally analyzed from the perspective of noise learning. The experimental results of semi -supervised attribute reduction and semi-supervised classification on UCI datasets show that our method is effective in handling partially labeled data and outperforms supervised models trained on all data with full supervision.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The study presents a TWD-based tri-training model for handling partially labeled data with heterogeneous attributes. Through semi-supervised reduction and multi-view training, the proposed model effectively handles partially labeled data and demonstrates better performance than supervised models in experiments.