Clustering-and-selection model for classifier combination
Ludmila Ilieva Kuncheva · 2002
We devise a simple clustering-and-selection algorithm based on a probabilistic interpretation of classifier selection. First, the data set is clustered into K clusters, and then the most successful classifier for a given cluster is nominated to label the inputs in the Voronoi cell of the cluster centroid. The proposed method is compared experimentally with the minimum, maximum, product and average. Also given are the results from the naive Bayes method, the behaviour-knowledge space (BKS) method, the best individual and the oracle.