Learning qualitative models from numerical data

• Published in: Artificial intelligence Vol. 175; no. 9; pp. 1604 - 1619
• Main Authors: Žabkar, Jure, Možina, Martin, Bratko, Ivan, Demšar, Janez
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.06.2011; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Derivatives; Exact sciences and technology; Expert systems; Learning; Learning and adaptive systems; Linear inference, regression; Mathematical analysis; Mathematical models; Mathematics; Monotone models; Partial derivatives; Probability and statistics; Qualitative modelling; Regression; Sciences and techniques of general use; Statistics; Regression; Monotone models; Qualitative modelling; Partial derivatives; Linear regression; Regression analysis; Modeling; Numerical data; Derivative; Qualitative analysis; Data models; Padé approximant; Artificial intelligence
• ISSN: 0004-3702; 1872-7921
• DOI: 10.1016/j.artint.2011.02.004

Qualitative models describe relations between the observed quantities in qualitative terms. In predictive modelling, a qualitative model tells whether the output increases or decreases with the input. We describe Padé, a new method for qualitative learning which estimates partial derivatives of the target function from training data and uses them to induce qualitative models of the target function. We formulated three methods for computation of derivatives, all based on using linear regression on local neighbourhoods. The methods were empirically tested on artificial and real-world data. We also provide a case study which shows how the developed methods can be used in practice.