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Probabilistic clustering for hierarchical multi-label classification of protein functions

Barros, Rodrigo C. and Cerri, Ricardo and Freitas, Alex A. and de Carvalho, André C.P.L.F. (2013) Probabilistic clustering for hierarchical multi-label classification of protein functions. In: Machine Learning and Knowledge Discovery in Databases European Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 385-400. ISBN 978-3-642-40990-5. E-ISBN 978-3-642-40991-2. (doi:10.1007/978-3-642-40991-2_25) (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/10.1007/978-3-642-40991-2_25

Abstract

Hierarchical Multi-Label Classification is a complex classification problem where the classes are hierarchically structured. This task is very common in protein function prediction, where each protein can have more than one function, which in turn can have more than one sub-function. In this paper, we propose a novel hierarchical multi-label classification algorithm for protein function prediction, namely HMC-PC. It is based on probabilistic clustering, and it makes use of cluster membership probabilities in order to generate the predicted class vector. We perform an extensive empirical analysis in which we compare our new approach to four different hierarchical multi-label classification algorithms, in protein function datasets structured both as trees and directed acyclic graphs. We show that HMC-PC achieves superior or comparable results compared to the state-of-the-art method for hierarchical multi-label classification.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-40991-2_25
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 17:10 UTC
Last Modified: 15 Oct 2019 14:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35629 (The current URI for this page, for reference purposes)
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