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Gaining Insights from Symbolic Regression Representations of Class Boundaries

Authors:
Ingo Schwab
Norbert Link

Keywords: Classification; Symbolic Regression; Knowledge Management; Data Mining; Pattern Recognition

Abstract:
In this paper, we propose a generalization of the wellknown regression analysis to fulfill supervised classification aiming to produce a learning model which best separates the class members of a labeled training set. The class boundaries are given by a separation surface which is represented by the level set of a model function. The separation boundary is defined by the respective equation. The model is represented by mathematical formulas and composed of an optimum set of expressions of a given superset. We show that this property gives human experts additional insight in the application domain. Furthermore, the representation in terms of mathematical formulas (e.g., the analytical model and its first and second derivative) adds additional value to the classifier and enables to answer questions, which other classifier approaches cannot. The symbolic representation of the models enables an interpretation by human experts.

Pages: 31 to 36

Copyright: Copyright (c) IARIA, 2012

Publication date: July 22, 2012

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-218-9

Location: Nice, France

Dates: from July 22, 2012 to July 27, 2012