On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers

The test set accuracy for ensembles of classifiers selected based on single measures of accuracy and diversity as well as combinations of such measures is investigated. It is found that by combining measures, a higher test set accuracy may be obtained than by using any single accuracy or diversity measure. It is further investigated whether a multi-criteria search for an ensemble that maximizes both accuracy and diversity leads to more accurate ensembles than by optimizing a single criterion. The results indicate that it might be more beneficial to search for ensembles that are both accurate and diverse. Furthermore, the results show that diversity measures could compete with accuracy measures as selection criterion.

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Tuve Löfström, Ulf Johansson, Henrik Boström
• Keywords: Diversity Measures, ICMLA 2008, Machine Learning, Single Measures, Test Set Accuracy