Reinforcement learning for rule extraction from a labeled dataset

Authors:
D. Vogiatzis;A. Stafylopatis
Affiliations:
Department of Electrical and Computer Engineering, National Technical University of Athens, 15773 Zographou, Athens, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, 15773 Zographou, Athens, Greece
Venue:
Cognitive Systems Research
Year:
2002

Citing 16
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Abstract

The article introduces a method, which is based on reinforcement learning, for extracting rules of the form if-then-else from a labeled data-set. The constituent parts of a rule are the input dimensions of the labeled data-set, each accompanied by an appropriate interval of activation, and a label which stands for class membership. Initially, the input space is partitioned using tiles. The algorithm tries to compose the largest possible orthogonal intervals out of tiles. After the creation of intervals for each dimension the rule receives credit for its classification ability. This credit with the aid of reinforcement will be used to improve its constituent parts. The effectiveness of the proposed method has been tested on five different classification problems: the Iris data set, the Concentric data, the 4 Gaussians, the Pima-Indians set and the Image Segmentation data set.