A hierarchical multiple classifier learning algorithm
This paper addresses the classification problem for applications with extensive amounts of data and a large number of features. The learning system developed utilizes a hierarchical multiple classifier scheme and is flexible, efficient, highly accurate and of low cost. The system has several novel features: (1) It uses a graph-theoretic clustering algorithm to group the training data into possibly overlapping cluster, each representing a dense region in the data space; (2) component classifiers trained on these dense regions are specialists whose probabilistic outputs are gated inputs to a super-classifier. Only those classifiers whose training clusters are most related to an unknown data instance send their outputs to the super-classifier; and (3) sub-class labelling is used to improve the classification of super-classes. The learning system achieves the goals of reducing the training cost and increasing the prediction accuracy compared to other multiple classifier algorithms. The system was tested on three large sets of data, two from the medical diagnosis domain and one from a forest cover classification problem. The results are superior to those obtained by several other learning algorithms.
class sub-labelling hierarchical classification system mega-data learning mixtures of classifiers