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An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions

Freitas, Alex A. (2014) An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions. In: 2014 25th International Workshop on Database and Expert Systems Applications. IEEE, pp. 64-68. ISBN 978-1-4799-5721-7. E-ISBN 978-1-4799-5722-4. (doi:10.1109/DEXA.2014.29) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/DEXA.2014.29

Abstract

The classification of protein and gene functions is a complex problem that is becoming more relevant as the number of sequenced genes and proteins increases. This work presents a modified version of the Extended Local Hierarchical Naive Bayes algorithm, which exploits the requirements of the original algorithm (single-path, mandatory-leaf-prediction hierarchical classification problems in tree-structured class hierarchies) to greatly improve classification run-time. We show that, considering 18 hierarchical classification datasets, the modified algorithm yields equivalent predictive performance and significantly improves run-time in the training and prediction phases.

Item Type: Book section
DOI/Identification number: 10.1109/DEXA.2014.29
Uncontrolled keywords: data mining, machine learning, hierarchical classification, bioinformatics, protein function prediction
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 15 Oct 2014 17:13 UTC
Last Modified: 25 Sep 2019 11:43 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/43432 (The current URI for this page, for reference purposes)
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