Skip to main content

ACO-based Bayesian network ensembles for the hierarchical classification of ageing-related proteins

Salama, Khalid M. and Freitas, Alex A. (2013) ACO-based Bayesian network ensembles for the hierarchical classification of ageing-related proteins. In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 11th European Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 80-91. ISBN 978-3-642-37188-2. E-ISBN 978-3-642-37189-9. (doi:10.1007/978-3-642-37189-9_8) (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
https://doi.org/10.1007/978-3-642-37189-9_8

Abstract

The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. Casting the task into a classification problem makes it challenging, since the classes (functions) to be predicted are hierarchically related, and a protein can have more than one function. One approach is to produce a set of local classifiers; each is responsible for discriminating between a subset of the classes in a certain level of the hierarchy. In this paper we tackle the hierarchical classification problem in a local fashion, by learning an ensemble of Bayesian network classifiers for each class in the hierarchy and combining their outputs with four alternative methods: a) selecting the best classifier, b) majority voting, c) weighted voting, and d) constructing a meta-classifier. The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. We empirically evaluate our proposed methods on an ageing-related protein dataset created for this research.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-642-37189-9_8
Uncontrolled keywords: ant colony optimization, data mining, machine learning, classifier ensemble, Bayesian network classifier
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 17 Jun 2013 17:22 UTC
Last Modified: 29 May 2019 10:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34306 (The current URI for this page, for reference purposes)
  • Depositors only (login required):