关键词: 权矩阵, 逻辑斯蒂回归, 特征选择, 非线性模型, 核函数

Abstract: Under supervised learning settings, problems that the dimension of the samples is typically larger than the number of samples are often encountered, i.e. many irrelevant features exist. In such case, the approaches that simultaneously achieve sparsely variable selection and better accuracy of classification are more preferable. In this paper, classification and feature slection algorithm based on kernel-weighted nonlinear logistic regression model is proposed. Each diagonal element of the weight diagonal matrix has a value between 0 and 1, which is as a learning parameter determined by optimization procedure, and fast alternative optimization methods are discussed. The proposed methods are tested on ten real-world datasets. The experimental results indicate that the proposed methods show high classification accuracies on these data sets than L1, L2, Lp norm regularization classifier algorithm of logistic regression model.

Key words: weighted matrix, logistic regression, feature selection, nonlinear model, kernel function