A multi-dimensional measure function for classifier performance

Evaluation of classifier performance is often based on statistical methods e.g. cross-validation tests. In these tests performance is often strongly related to or solely based on the accuracy of the classifier on a limited set of instances. The use of measure functions has been suggested as a promis...

Full description

Saved in:
Bibliographic Details
Main Authors: Lavesson, N., Davidsson, P.
Format: Conference Proceeding
Language:English
Subjects:
classifier
cross-validation
Data mining
Electronic mail
evaluation
Genetic algorithms
Machine learning
measure function
multi-dimensional
Neural networks
Partitioning algorithms
Sections
Statistical analysis
Telephony
Testing
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Evaluation of classifier performance is often based on statistical methods e.g. cross-validation tests. In these tests performance is often strongly related to or solely based on the accuracy of the classifier on a limited set of instances. The use of measure functions has been suggested as a promising approach to deal with this limitation. However, no usable implementation of a measure function has yet been presented. This article presents such an implementation and demonstrates its usage through a set of experiments. The results indicate that there are cases for which measure functions may be able to capture important aspects of the evaluated classifier that cannot be captured by cross-validation tests.
DOI:10.1109/IS.2004.1344802