关键词: 偏最小二乘回归, k近邻, 噪声样本, 特征选择, 鲁棒性

Abstract: A Robust Feature Selection and Classification algorithm based on Partial Least Squares Regression (RFSC-PLSR) was proposed to solve the problem of redundancy and multi-collinearity between features in feature selection. Firstly, the consistency coefficient of sample class based on neighborhood estimation was defined. Then, the k Nearest Neighbor (kNN) operation was used to select the conservative samples with local class structure stability, and the partial least squares regression model was used to construct the robust feature selection. Finally, a partial least squares classification model was constructed using the class consistency coefficient and the preferred feature subset for all samples from a global structure perspective. Five data sets of different dimensions were selected from the UCI database for numerical experiments. The experimental results show that compared with four typical classifiers-Support Vector Machine (SVM), Naive Bayes (NB), Back-Propagation Neural Network (BPNN) and Logistic Regression (LR), RFSC-PLSR is more efficient in low-dimensional, medium-dimension, high-dimensional and other different cases, and shows stronger competitiveness in classification accuracy, robustness and computational efficiency.

Key words: Partial Least Squares Regression (PLSR), k Nearest Neighbor (kNN), noise sample, feature selection, robust