An ensemble of classifiers succeeds in improving the accuracy of the whole when thecomponent classifiers are both diverse and accurate. Diversity is required to ensure that theclassifiers make uncorrelated errors. Theoretical and experimental approaches from previousresearch show very low correlation between ensemble accuracy and diversity measure.Introducing Proposed Compound diversity functions by Albert Hung-Ren KO and RobertSabourin, (2009), by combining diversities and performances of individual classifiers exhibitstrong correlations between the diversities and accuracy. To be consistent with existingarguments compound diversity of measures are evaluated and compared with traditionaldiversity measures on different problems. Evaluating diversity of errors and comparison withmeasures are significant in this study. The results show that compound diversity measuresare better than ordinary diversity measures. However, the results further explain evaluation ofdiversity of errors on available data. / Program: Magisterutbildning i informatik