Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins

Pappa, Gisele L. and Freitas, Alex A. (2009) Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. Intelligent Data Analysis, 13 (2). pp. 243-259. ISSN 1088-467X. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.3233/IDA-2009-0366

Abstract

It is well-known that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammar-based genetic programming (GGP) system to perform "algorithm construction". The GGP is used to build a complete rule induction algorithm tailored to 5 well-known UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with well-known human-designed rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the human-designed algorithms in discovering accurate rules predicting the minority class - whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class

Item Type: Article
Uncontrolled keywords: data mining, classification, bioinformatics, genetic programming, rule induction
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:14
Last Modified: 20 May 2014 08:15
Resource URI: http://kar.kent.ac.uk/id/eprint/24096 (The current URI for this page, for reference purposes)
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