Sparse concordance‐based ordinal classification
提出一种基于一致性函数的序分类新方法，通过惩罚平滑优化实现变量选择，并给出非参数类条件概率估计，理论证明渐近性质，模拟和实际数据表明分类精度优于现有方法。
Abstract Ordinal classification is an important area in statistical machine learning, where labels exhibit a natural order. One of the major goals in ordinal classification is to correctly predict the relative order of instances. We develop a novel concordance‐based approach to ordinal classification, where a concordance function is introduced and a penalized smoothed method for optimization is designed. Variable selection using the penalty is incorporated for sparsity considerations. Within the set of classification rules that maximize the concordance function, we find optimal thresholds to predict labels by minimizing a loss function. After building the classifier, we derive nonparametric estimation of class conditional probabilities. The asymptotic properties of the estimators as well as the variable selection consistency are established. Extensive simulations and real data applications show the robustness and advantage of the proposed method in terms of classification accuracy, compared with other existing methods.