Combination of multiple classifiers, commonly referred to as an classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. One popular approach to building such a combination of classifiers is known as stacking and is based on a meta-learning approach. In this work we investigate a modified version of stacking based on cluster analysis. Instances from a validation set are firstly classified by all base classifiers. The classified results are then grouped into a number of clusters. Two instances are considered as being similar if they are correctly/incorrectly classified to the same class by the same group of classifiers. When classifying a new instance, the approach attempts to find the cluster to which it is closest. The method outperformed individual classifiers, classification by a clustering method and the majority voting method.
|Title of host publication||Proceeding MCS'11 Proceedings of the 10th international conference on Multiple classifier systems|
|Publication status||Published - 2011|
Jurek, A., Bi, Y., Wu, S., & Nugent, C. D. (2011). Classification by Cluster Analysis: A New Meta-Learning Based Approach. In Proceeding MCS'11 Proceedings of the 10th international conference on Multiple classifier systems (pp. 259-268). Springer.