A bayesian metric for evaluating machine learning algorithms

Authors:
Lucas R. Hope;Kevin B. Korb
Affiliations:
School of Computer Science, and Software Engineering, Monash University, Clayton, VIC, Australia;School of Computer Science, and Software Engineering, Monash University, Clayton, VIC, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 9
Cited 3

Information-Based Evaluation Criterion for Classifier's Performance

Machine Learning
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Lazy Learning of Bayesian Rules

Machine Learning
Robust Classification for Imprecise Environments

Machine Learning
Bayesian Information Reward

AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Bayesian Artificial Intelligence

Bayesian Artificial Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Building classifiers using Bayesian networks

AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Discovering and Exploiting Causal Dependencies for Robust Mobile Context-Aware Recommenders

IEEE Transactions on Knowledge and Data Engineering
Decision analysis of data mining project based on Bayesian risk

Expert Systems with Applications: An International Journal
Incorporating expert knowledge when learning Bayesian network structure: A medical case study

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

How to assess the performance of machine learning algorithms is a problem of increasing interest and urgency as the data mining application of myriad algorithms grows Rather than predictive accuracy, we propose the use of information-theoretic reward functions The first such proposal was made by Kononenko and Bratko Here we improve upon our alternative Bayesian metric, which provides a fair betting assessment of any machine learner We include an empirical analysis of various Bayesian classification learners.