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An Extended Local Hierarchical Classifier for Prediction of Protein and Gene Functions.

de Campos Merschmann, Luiz Henrique and Freitas, Alex Alves (2013) An Extended Local Hierarchical Classifier for Prediction of Protein and Gene Functions. In: Bellatreche, Ladjel and Mohania, Mukesh K., eds. Data Warehousing and Knowledge Discovery 15th International Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 159-171. ISBN 978-3-642-40130-5. E-ISBN 978-3-642-40131-2. (doi:10.1007/978-3-642-40131-2_14) (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/10.1007/978-3-642-40131-2_14

Abstract

Gene function prediction and protein function prediction are complex classification problems where the functional classes are structured according to a predefined hierarchy. To solve these problems, we propose an extended local hierarchical Naive Bayes classifier, where a binary classifier is built for each class in the hierarchy. The extension to conventional local approaches is that each classifier considers both the parent and child classes of the current class. We have evaluated the proposed approach on eight protein function and ten gene function hierarchical classification datasets. The proposed approach achieved somewhat better predictive accuracies than a global hierarchical Naive Bayes classifier.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-40131-2_14
Uncontrolled keywords: data mining, machine learning, hierarchical classification, bioinformatics
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 24 Oct 2013 16:53 UTC
Last Modified: 15 Oct 2019 14:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35627 (The current URI for this page, for reference purposes)
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