Ant Programming Algorithms for Classification

Ant Programming Algorithms for Classification

Juan Luis Olmo (University of Córdoba, Spain), José Raúl Romero (University of Córdoba, Spain) and Sebastián Ventura (University of Córdoba, Spain)
Copyright: © 2014 |Pages: 22
DOI: 10.4018/978-1-4666-6078-6.ch005

Abstract

Ant programming is a kind of automatic programming that generates computer programs by using the ant colony metaheuristic as the search technique. It has demonstrated good generalization ability for the extraction of comprehensible classifiers. To date, three ant programming algorithms for classification rule mining have been proposed in the literature: two of them are devoted to regular classification, differing mainly in the optimization approach, single-objective or multi-objective, while the third one is focused on imbalanced domains. This chapter collects these algorithms, presenting different experimental studies that confirm the aptitude of this metaheuristic to address this data-mining task.
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The Gbap Algorithm: Grammar-Based Ant Programming

This section introduces the first AP algorithm for classification, called GBAP, which is based on the use of a context-free grammar (CFG) for ensuring the generation of individuals syntactically valid, as well as the other AP algorithms presented in this work. The algorithm evolves a population of rules from the training set that are combined at the end of the last generation into a decision-list like classifier. Then, the model induced is test over the test set and the results obtained are reported. The flowchart of GBAP is shown in Figure 1, and its characteristics are described in the following subsections.

Figure 1.

Flowchart of GBAP

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