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Extracting Hierarchies from Data Clusters for Better Classification

Author(s): German Sapozhnikov | Alexander Ulanov

Journal: Algorithms
ISSN 1999-4893

Volume: 5;
Issue: 4;
Start page: 506;
Date: 2012;
Original page

Keywords: multi-label classification | hierarchical classification | clustering

In this paper we present the PHOCS-2 algorithm, which extracts a “Predicted Hierarchy Of ClassifierS”. The extracted hierarchy helps us to enhance performance of flat classification. Nodes in the hierarchy contain classifiers. Each intermediate node corresponds to a set of classes and each leaf node corresponds to a single class. In the PHOCS-2 we make estimation for each node and achieve more precise computation of false positives, true positives and false negatives. Stopping criteria are based on the results of the flat classification. The proposed algorithm is validated against nine datasets.
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