On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets

被引:50
作者
Fernandez, Alberto [1 ]
Jose del Jesus, Maria [2 ]
Herrera, Francisco [1 ]
机构
[1] Univ Granada, Dept Comp Sci & AI, E-18071 Granada, Spain
[2] Univ Jaen, Dept Comp Sci, Jaen, Spain
关键词
Fuzzy rule-based classification systems; Inference mechanism; Parametric conjunction operators; Genetic fuzzy systems; Imbalanced data-sets; FRAMEWORK; CLASSIFIERS; NETWORK;
D O I
10.1016/j.eswa.2009.02.048
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Classification with imbalanced data-sets Supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real applications. In this paper, we will work with fuzzy rule based classification systems using a preprocessing step in order to deal with the class imbalance. Our aim is to analyze the behaviour of fuzzy rule based classification systems in the framework of imbalanced data-sets by means of the application of an adaptive inference system with parametric conjunction operators. Our results shows empirically that the use of the this parametric conjunction operators implies a higher performance for all data-sets with different imbalanced ratios. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:9805 / 9812
页数:8
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