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

Extracting Hierarchies from Data Clusters for Better Classification

German Sapozhnikov 1,* email and Alexander Ulanov 2,* email
1 Saint Petersburg State Polytechnical University, Polytechnicheskaya 29, Saint Petersburg 194064, Russia 2 HP Labs Russia, Artilleriyskaya 1, Saint Petersburg 191014, Russia
* Authors to whom correspondence should be addressed.
Received: 2 July 2012; in revised form: 24 September 2012 / Accepted: 17 October 2012 / Published: 23 October 2012
(This article belongs to the Special Issue Selected Papers from Text Mining Workshop at the 2012 SIAM International Conference on Data Mining)
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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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