NDLI: A first approach for cost-sensitive classification with linguistic Genetic Fuzzy Systems in imbalanced data-sets

Content Provider	IEEE Xplore Digital Library
Author	López, V. Fernández, A. Herrera, F.
Copyright Year	2010
Description	Author affiliation: Dept. of Computer Science and A. I., University of Granada, Granada, Spain (López, V.) \|\| Dept. of Computer Science and A.I., University of Granada, Granada, Spain (Herrera, F.) \|\| Dept. of Computer Science, University of Jaén, Jaén, Spain (Fernández, A.)
Abstract	Classification in imbalanced domains has become one of the most relevant problems within the area of Machine Learning at the present. This problem has raised in significance due to its presence in many real applications and it occurs when the distribution of the available examples to carry out the learning process is very different between the classes (often for binary class data-sets). Usually, the underrepresented class is the concept of the most interest for the problem, being the cost derived from a misclassification of these examples much higher than that of the remaining examples. In this work we analyze the behaviour of a cost-sensitive learning method for Fuzzy Rule Based Classification Systems in the scenario of high imbalanced data-sets. Specifically, we focus on one representative rule learning approach for Genetic Fuzzy Systems, the Fuzzy Hybrid Genetics-Based Machine Learning algorithm. The experimental results show how our cost-sensitive approach in this type of domains will help us to obtain very accurate solutions in shorter training times and also with a lower complexity with respect to other possibilities proposed for classification with imbalanced problems such as the use of preprocessing to rebalance the class distribution.
Starting Page	676
Ending Page	681
File Size	185123
Page Count	6
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687187
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Measurement Learning systems Genetic Fuzzy Systems Cost-Sensitive Machine learning Imbal-anced Data-sets Genetics Intelligent systems Fuzzy Rule Based Classification Systems Pragmatics
Content Type	Text
Resource Type	Article

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