Learning qualitative models from numerical data: extended abstract

Authors:
Jure Žabkar;Martin Možina;Ivan Bratko;Janez Demšar
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Slovenia
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

Qualitative models are predictive models that describe how changes in values of input variables affect the output variable in qualitative terms, e.g. increasing or decreasing. 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.