The class imbalance problem in learning classifier systems: a preliminary study

Authors:
Albert Orriols;Ester Bernadó-Mansilla
Affiliations:
Universitat Ramon Llull, Quatre Camins, Barcelona, Spain;Universitat Ramon Llull, Quatre Camins, Barcelona, Spain
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

Citing 6
Cited 13

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Adaptive Fraud Detection

Data Mining and Knowledge Discovery
Accuracy-based learning classifier systems: models, analysis and applications to classification tasks

Evolutionary Computation
The class imbalance problem: A systematic study

Intelligent Data Analysis
Classifier fitness based on accuracy

Evolutionary Computation
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research

Bounding XCS's parameters for unbalanced datasets

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Towards Adapting XCS for Imbalance Problems

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A grid data mining architecture for learning classifier systems

WSEAS Transactions on Computers
Supervised learning classifier systems for grid data mining

CIS'09 Proceedings of the international conference on Computational and information science 2009
Extracting informative images from web news pages via imbalanced classification

MM '09 Proceedings of the 17th ACM international conference on Multimedia
Facetwise analysis of XCS for problems with class imbalances

IEEE Transactions on Evolutionary Computation
Evolutionary data analysis for the class imbalance problem

Intelligent Data Analysis
Fitness functions in genetic programming for classification with unbalanced data

AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
An empirical study of the behavior of classifiers on imbalanced and overlapped data sets

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Predict on-shelf product availability in grocery retailing with classification methods

Expert Systems with Applications: An International Journal
Machine learning techniques and mammographic risk assessment

IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Semantic entity-relationship model for large-scale multimedia news exploration and recommendation

MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Combined effects of class imbalance and class overlap on instance-based classification

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

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-balance datasets. However, 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 datasets 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.