Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery

Brookhouse, James and Otero, Fernando E.B. (2016) Using an Ant Colony Optimization Algorithm for Monotonic Regression Rule Discovery. In: Genetic and Evolutionary Computation Conference (GECCO 2016), 20-24 July 2016, Denver, United States. (doi:https://doi.org/10.1145/2908812.2908896) (Full text available)

Abstract

Many data mining algorithms do not make use of existing domain knowledge when constructing their models. This can lead to model rejection as users may not trust models that behave contrary to their expectations. Semantic constraints provide a way to encapsulate this knowledge which can then be used to guide the construction of models. One of the most studied semantic constraints in the literature is monotonicity, however current monotonically-aware algorithms have focused on ordinal classification problems. This paper proposes an extension to an ACO-based regression algorithm in order to extract a list of monotonic regression rules. We compared the proposed algorithm against a greedy regression rule induction algorithm that preserves monotonic constraints and the well-known M5’ Rules. Our experiments using eight publicly available data sets show that the proposed algorithm successfully creates monotonic rules while maintaining predictive accuracy.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 29 Apr 2016 09:00 UTC
Last Modified: 15 Nov 2016 09:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55191 (The current URI for this page, for reference purposes)
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