Volume 11, Issue 2 (3-2015)                   JSDP 2015, 11(2): 111-125 | Back to browse issues page

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mahdizadeh M, eftekhari M. A new fuzzy rules weighting approach based on Genetic Programming for imbalanced classification . JSDP. 2015; 11 (2) :111-125
URL: http://jsdp.rcisp.ac.ir/article-1-28-en.html
shahid bahonar university
Abstract:   (4643 Views)
In classification problems, we often encounter datasets with different percentage of patterns (i.e. classes with a high pattern percentage and classes with a low pattern percentage). These problems are called “classification Problems with imbalanced data-sets”. Fuzzy rule based classification systems are the most popular fuzzy modeling systems used in pattern classification problems. Rule weights have been usually used to improve the classification accuracy and fuzzy versions of confidence and support merits have been widely used for rules weighting in fuzzy rule based classifiers. In this paper, we propose an evolutionary approach based on genetic programming to generate weighting expressions. For producing expressions confidence, support, lift and recall merits are used as terminals of genetic programming. Experiments are performed over 20 imbalanced KEELchr('39')s datasets and the results are analyzed using statistical tests. The results show that the proposed method improves the classification accuracy of FRBCS.
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Type of Study: Research | Subject: Paper
Received: 2013/04/27 | Accepted: 2014/09/21 | Published: 2015/03/22 | ePublished: 2015/03/22

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