TY - GEN
T1 - The class imbalance problem in UCS classifier system
T2 - A preliminary study
AU - Orriols-Puig, Albert
AU - Bernadó-Mansilla, Ester
PY - 2007
Y1 - 2007
N2 - The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. But this commitment is not always true, since it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced dataseis and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.
AB - The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. But this commitment is not always true, since it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced dataseis and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.
UR - http://www.scopus.com/inward/record.url?scp=38049103501&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71231-2_12
DO - 10.1007/978-3-540-71231-2_12
M3 - Conference contribution
AN - SCOPUS:38049103501
SN - 9783540712305
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 161
EP - 180
BT - Learning Classifier Systems - International Workshops, IWLCS 2003-2005, Revised Selected Papers
PB - Springer Verlag
ER -