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.

由于半监督学习相对于监督学习的优势，吸引了研究人员。本文提出了一种快速的多类别分类算法，称为加权三元决策结构（WTDS）。WTDS是一种通用算法，可以将任何二进制分类器扩展到多类别框架中。这项工作还提出了一种新颖的半监督二元分类器，称为加权拉普拉斯最小二乘双支持向量机，并使用WTDS对其进行了扩展。新型的半监督分类器通过制定一对无约束最小化问题来求解，这些问题可以作为线性方程组求解。WTDS充分利用了分类器的优势，并以决策结构的形式有效地构建了多分类器模型。在分类准确性和时间复杂度方面，WTDS优于其他最新的多类别方法。为了确认所提出算法的可行性和有效性，对基准UCI数据集进行了实验。