A Visualization-Based Exploratory Technique for Classifier Comparison with Respect to Multiple Metrics and Multiple Domains

Authors:
Rocío Alaiz-Rodríguez;Nathalie Japkowicz;Peter Tischer
Affiliations:
Dpto. de Ingeniería Eléctrica y de Sistemas, Universidad de León, Spain;School of Information Technology and Engineering, University of Ottawa, Canada;Clayton School of Information Technology, Monash University, Australia
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

Citing 3
Cited 2

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Data mining in metric space: an empirical analysis of supervised learning performance criteria

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Projection-Based Framework for Classifier Performance Evaluation

ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I

On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

Knowledge-Based Systems
Using classifier performance visualization to improve collective ranking techniques for biomedical abstracts classification

AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Classifier performance evaluation typically gives rise to a multitude of results that are difficult to interpret. On the one hand, a variety of different performance metrics can be applied, each adding a little bit more information about the classifiers than the others; and on the other hand, evaluation must be conducted on multiple domains to get a clear view of the classifier's general behaviour.