关键词: 有序回归, 最小平方回归, 边际特征扰动, 双重扰动

Abstract: Ordinal regression is a special machine learning paradigm whose purpose is to classify patterns by using between-class natural ordinal scale. Many ordinal regression algorithms have been proposed. However, their performance will largely be constrained when facing the dataset with the limited size. To remedy the shortcoming of finite dataset, inspired by recently-proposed marginalized corrupted features, this paper develops the least squares ordinal regression using doubly corrupted features (LSOR-DCF) which is based on least squares ordinal regression by corrupting both the samples using random noise from known distributions and the labels using deterministic biases. The experimental results demonstrate the superiority of LSOR-DCF in performance, especially in the small data sets, to related methods without adding either noise in samples or corrupted noise in samples and labels alone.

Key words: ordinal regression, least squares regression, marginalized corrupted features, double corruption