Revisiting the Dissimilarity Representation in the Context of Regression

IEEE Access

Vicente Garcia¹  J. Salvador Sanchez²  Rafael Martinez-Pelaez³  Luis C. Mendez-Gonzalez⁴ 

[1] Department of Electrical and Computer Engineering, Universidad Aut&x00F3;noma de Ciudad Ju&x00E1;rez, Ciudad Ju&x00E1;rez, M&x00E9;

关键词: Data complexity;    dissimilarity representation;    linear models;    regression;   

DOI  :  10.1109/ACCESS.2021.3130127

来源: DOAJ

【 摘 要 】

In machine learning, a natural way to represent an instance is by using a feature vector. However, several studies have shown that this representation may not accurately characterize an object. For classification problems, the dissimilarity paradigm has been proposed as an alternative to the standard feature-based approach. Encoding each object by pairwise dissimilarities has been demonstrated to improve the data quality because it mitigates some complexities such as class overlap, small disjuncts, and low-sample size. However, its suitability and performance when applied to regression problems have not been fully explored. This study redefines the dissimilarity representation for regression. To this end, we have carried out an extensive experimental evaluation on 34 datasets using two linear regression models. The results show that the dissimilarity approach decreases the error rates of both the traditional linear regression and the linear model with elastic net regularization, and it also reduces the complexity of most regression datasets.

【 授权许可】

Unknown