Algorithms 2012, 5(4), 506-520; doi:10.3390/a5040506

Extracting Hierarchies from Data Clusters for Better Classification

1,* email and 2,* email
Received: 2 July 2012; in revised form: 24 September 2012 / Accepted: 17 October 2012 / Published: 23 October 2012
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: 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.
Keywords: multi-label classification; hierarchical classification; clustering
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MDPI and ACS Style

Sapozhnikov, G.; Ulanov, A. Extracting Hierarchies from Data Clusters for Better Classification. Algorithms 2012, 5, 506-520.

AMA Style

Sapozhnikov G, Ulanov A. Extracting Hierarchies from Data Clusters for Better Classification. Algorithms. 2012; 5(4):506-520.

Chicago/Turabian Style

Sapozhnikov, German; Ulanov, Alexander. 2012. "Extracting Hierarchies from Data Clusters for Better Classification." Algorithms 5, no. 4: 506-520.

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