A Case Study of SVM Extension Techniques on classification of Imbalanced Data

K. K. Lee, C. J. Harris, S. R. Gunn, P. A. S. Reed

Research output: Contribution to conferencePaper

Abstract

In many classification problems the data is imbalanced, that is the class priors are different. Here, we consider the classification problem of fatigue crack initiation in automotive camshafts, where this imbalance is significant. The extension techniques of Support Vector Machine (SVM) - the Control Sensitivity (CSSVM) and Adaptive Margin (AMSVM) - which offer different ways of dealing with imbalanced data was investigated. Geometric mean was used to evaluate the performance of the model. The CSSVM has outperformed the AMSVM. The use of different kernels did not produce significant changes in the results. The ratio between the misclassification cost and the training size for each class is very similar, indicating a strong relationship between them.
Original languageEnglish
Pages309-314
Number of pages6
Publication statusPublished - Feb 2001
EventCongress on Neural Networks and Applications, Fuzzy Sets and Fuzzy Systems and Evolutionary Computing - Puerto De La Cruz, Tenerife, Canary Islands, Spain
Duration: 11 Feb 200115 Feb 2001

Conference

ConferenceCongress on Neural Networks and Applications, Fuzzy Sets and Fuzzy Systems and Evolutionary Computing
CountrySpain
CityPuerto De La Cruz, Tenerife, Canary Islands
Period11/02/0115/02/01

Cite this