The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study

Author

Visa, Sofia ; Ralescu, Anca

Author_Institution

Dept. of Electr. Comput. & Eng. Comput. Sci., Cincinnati Univ., OH

fYear

2005

fDate

25-25 May 2005

Firstpage

749

Lastpage

754

Abstract

This study evaluates the robustness of a fuzzy classifier when class distribution of the training set varies. The analysis of the results is based on the classification accuracy and ROC curves. The experimental results reported here show that fuzzy classifiers are less variant with the class distribution and less sensitive to the imbalance factor than decision trees

Keywords

fuzzy set theory; learning (artificial intelligence); pattern classification; statistical analysis; ROC curve; data class distribution; decision tree; fuzzy classifier; imbalance factor; training set; Classification tree analysis; Costs; Decision trees; Error correction; Fuzzy sets; Machine learning algorithms; Minimization methods; Robustness; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452488

Filename

1452488

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3401404