Evolutionary Computation for the Automated Design of Category Functions for Fuzzy ART: An Initial Exploration
Fuzzy Adaptive Resonance Theory (ART) is a classic unsupervised learning algorithm. Its performance on a particular clustering problem is sensitive to the suitability of the category function for said problem. However, classic Fuzzy ART employs a fixed category function and thus is unable to benefit from the potential to adjust its category function. This paper presents an exploration into employing evolutionary computation for the automated design of category functions to obtain significantly enhanced Fuzzy ART performance through tailoring to specific problem classes. We employ a genetic programming powered hyper-heuristic approach where the category functions are constructed from a set of primitives constituting those of the original Fuzzy ART category function as well as additional hand-selected primitives. Results are presented for a set of experiments on benchmark classification tasks from the UCI Machine Learning Repository, demonstrating that tailoring Fuzzy ART's category function can achieve statistically significant superior performance on the testing datasets in stratified 10-fold cross-validation procedures. We conclude with discussing the results and placing them in the context of being a first step towards automating the design of entirely new forms of ART.
I. Elnabarawy et al., "Evolutionary Computation for the Automated Design of Category Functions for Fuzzy ART: An Initial Exploration," Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017, Berlin, Germany), pp. 1133-1140, Association for Computing Machinery (ACM), Jul 2017.
The definitive version is available at https://doi.org/10.1145/3067695.3082056
Genetic and Evolutionary Computation Conference, GECCO 2017 (2017: Jul. 15-19, Berlin, Germany)
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Adaptive resonance theory; Adjusted rand index; Clustering; Evolutionary computing; Genetic programming; Hyper-heuristics; Unsupervised learning
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 Association for Computing Machinery (ACM), All rights reserved.
01 Jul 2017