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IJCAI
2007

Constructing New and Better Evaluation Measures for Machine Learning

8 years 11 months ago
Constructing New and Better Evaluation Measures for Machine Learning
Evaluation measures play an important role in machine learning because they are used not only to compare different learning algorithms, but also often as goals to optimize in constructing learning models. Both formal and empirical work has been published in comparing evaluation measures. In this paper, we propose a general approach to construct new measures based on the existing ones, and we prove that the new measures are consistent with, and finer than, the existing ones. We also show that the new measure is more correlated to RMS (Root Mean Square error) with artificial datasets. Finally, we demonstrate experimentally that the greedy-search based algorithm (such as artificial neural networks) trained with the new and finer measure usually can achieve better prediction performance. This provides a general approach to improve the predictive performance of existing learning algorithms based on greedy search.
Jin Huang, Charles X. Ling
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where IJCAI
Authors Jin Huang, Charles X. Ling
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