Ensemble member selection using multi-objective optimization

— Both theory and a wealth of empirical studies have established that ensembles are more accurate than single predictive models. Unfortunately, the problem of how to maximize ensemble accuracy is, especially for classification, far from solved. In essence, the key problem is to find a suitable criterion, typically based on training or selection set performance, highly correlated with ensemble accuracy on novel data. Several studies have, however, shown that it is difficult to come up with a single measure, such as ensemble or base classifier selection set accuracy, or some measure based on diversity, that is a good general predictor for ensemble test accuracy. This paper presents a novel technique that for each learning task searches for the most effective combination of given atomic measures, by means of a genetic algorithm. Ensembles built from either neural networks or random forests were empirically evaluated on 30 UCI datasets. The experimental results show that when using the g...

• Type: Conference
• Year: 2009
• Where: CIDM
• Authors: Tuve Löfström, Ulf Johansson, Henrik Boström
• Keywords: Artificial Intelligence, CIDM 2009, Ensemble Accuracy, Ensemble Test Accuracy, Selection Set Accuracy