Semi-supervised Weighted Ternary Decision Structure for Multi-category Classification

摘要

Semi-supervised learning has attracted researchers due to its advantages over supervised learning. In this paper, an extremely fast multi-category classification algorithm, termed as weighted ternary decision structure (WTDS) is proposed. WTDS is a generic algorithm that can extend any binary classifier into multi-category framework. This work also proposes a novel semi-supervised binary classifier termed as Weighted Laplacian least-squares twin support vector machine which is further extended using WTDS. The novel semi-supervised classifier obtains the solution by formulating a pair of Unconstrained Minimization Problems which are solved as systems of linear equation. WTDS takes advantage of the strengths of the classifier and efficiently constructs the multi-category classifier model in the form of a decision structure. WTDS outperforms other state-of-the-art multi-category approaches in terms of classification accuracy and time complexity. To confirm the feasibility and efficacy of proposed algorithm, experiments are conducted on benchmark UCI datasets.