Learning classifier system using both labeled and unlabeled data

Authors:
Chi Su;Yang Gao;Chun Cao
Affiliations:
Nanjing University, Nanjing, China;Nanjing University, Nanjing, China;Nanjing University, Nanjing, China
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 3
Cited 0

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Abstract

In this paper, we propose a Semi-UCS, which is an extension of the classical sUpervised Classifier System (UCS) [1] for semi-supervised learning tasks. A UCS works under a supervised learning scheme and uses only labeled data to train the system. In the Semi-UCS, we add an additional semi-supervised learning component to the original UCS, enablingthe LCS to learn from both labeled and unlabeled data. We provide three methods of how this semi-supervised learning component can be implemented: self-learning method, k-NN distance measure method and tri-training method. The Semi-UCS enlarges UCS's' application domains into semi-supervised settings and is a great addition to the LCS's model family. Experimental results on benchmark data sets of UCI repository have shown that Semi-UCS reaches a good performance for semi-supervised learning tasks.