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Cost-sensitive XCS classifier system addressing imbalance problems
Author(s)
Thach, Nguyen Huy
Rojanavasu, Porntep
Pinngern, Ouen
Date Issued
December 1, 2008
Type
Conference Paper
Abstract
The class imbalance problem has been recognized as a crucial problem in machine learning and data mining. Learning systems tend to be biased towards the majority class and thus have poor generalization for the minority class instances. This paper analyses the imbalance problem in accuracy-based learning classifier systems. In particular, we propose a novel approach based on XCS classifier system and cost-sensitive learning. In our approach, the reward value of correctly identifying the positive (rare) class outweighs the value of correctly identifying the common class. This research provides guidelines to set reward base on the dataset imbalance ratio and a method to calculate reward online base on the information collected by XCS during training is also proposed. Experimental results on synthetic and real-life datasets show that, with appropriate reward settings, XCS is robust to class imbalances. © 2008 IEEE.
Citation
Proceedings 5th International Conference on Fuzzy Systems and Knowledge Discovery Fskd 2008, 2, 132-136, 2008