Pattern-Aided Regression Modeling and Prediction Model Analysis

TitlePattern-Aided Regression Modeling and Prediction Model Analysis
Publication TypeJournal Article
Year of Publication2015
AuthorsGuozhu Dong, Vahid Taslimitehrani
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue9
Pagination2452-2465
Date Published11/2015
Accession Number15381883
KeywordsCorrelation and regression analysis, Data Mining, error analysis, mining methods and algorithms, model validation and analysis
Abstract

This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships, which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

DOI10.1109/TKDE.2015.2411609