Fuzzy ROC curves for unsupervised nonparametric ensemble techniques

This paper explores a novel ensemble technique for unsupervised classification using nonparametric statistics. Multiple classification systems (MCS), or ensemble techniques, involve considering several classification methods or multiple outputs from the same method and devising techniques to reach a...

Full description

Saved in:
Bibliographic Details
Main Authors: Evangelista, P.E., Embrechts, M.J., Bonissone, P., Szymanski, B.K.
Format: Conference Proceeding
Language:English
Subjects:
Area measurement
Computer science
Electronic mail
Fuzzy logic
Machine learning
Statistics
Support vector machine classification
Support vector machines
Systems engineering and theory
Unsupervised learning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper explores a novel ensemble technique for unsupervised classification using nonparametric statistics. Multiple classification systems (MCS), or ensemble techniques, involve considering several classification methods or multiple outputs from the same method and devising techniques to reach a decision. The performance of a binary classification system can be measured on a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) is exactly the Wilcoxon rank sum or Mann-Whitney U statistic, both of which are nonparametric statistics based upon ranked data. Successful performance of an unsupervised ensemble can be measured through the AUC, and the performance of different aggregation techniques for the combination of the multiple classification system decision values, or rankings in this paper, is illustrated. Aggregation techniques are based upon fuzzy logic theory, creating the fuzzy ROC curve. The one-class SVM is utilized for the unsupervised classification.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2005.1556410