Welcome to the InfoSci Platform
Reference Hub5
Ant Colony Algorithms for Data Classification

Ant Colony Algorithms for Data Classification

Alex A. Freitas, Rafael S. Parpinelli, Heitor S. Lopes
Copyright: © 2009 |Pages: 6
ISBN13: 9781605660264|ISBN10: 1605660264|EISBN13: 9781605660271
DOI: 10.4018/978-1-60566-026-4.ch027
Cite Chapter Cite Chapter

MLA

Freitas, Alex A., et al. "Ant Colony Algorithms for Data Classification." Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, D.B.A., IGI Global, 2009, pp. 154-159. https://doi.org/10.4018/978-1-60566-026-4.ch027

APA

Freitas, A. A., Parpinelli, R. S., & Lopes, H. S. (2009). Ant Colony Algorithms for Data Classification. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Second Edition (pp. 154-159). IGI Global. https://doi.org/10.4018/978-1-60566-026-4.ch027

Chicago

Freitas, Alex A., Rafael S. Parpinelli, and Heitor S. Lopes. "Ant Colony Algorithms for Data Classification." In Encyclopedia of Information Science and Technology, Second Edition, edited by Mehdi Khosrow-Pour, D.B.A., 154-159. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-026-4.ch027

Export Reference

Mendeley
Favorite

Abstract

Ant colony optimization (ACO) is a relatively new computational intelligence paradigm inspired by the behavior of natural ants (Dorigo & Stutzle, 2004). Ants often find the shortest path between a food source and the nest of the colony without using visual information. In order to exchange information about which path should be followed, ants communicate with each other by means of a chemical substance called pheromone. As ants move, a certain amount of pheromone is dropped on the ground, creating a pheromone trail. The more ants that follow a given trail, the more attractive that trail becomes to be followed by other ants. This process involves a loop of positive feedback, in which the probability that an ant chooses a path is proportional to the number of ants that have already passed by that path. Hence, individual ants, following very simple rules, interact to produce an intelligent behavior at the higher level of the ant colony. In other words, intelligence is an emergent phenomenon. In this article we present an overview of Ant-Miner, an ACO algorithm for discovering classification rules in data mining (Parpinelli, Lopes, & Freitas, 2002a, 2002b), as well as a review of several Ant-Miner variations and related ACO algorithms. All the algorithms reviewed in this article address the classification task of data mining. In this task each case (record) of the data being mined consists of two parts: a goal attribute, whose value is to be predicted, and a set of predictor attributes. The aim is to predict the value of the goal attribute for a case, given the values of the predictor attributes for that case (Fayyad, Piatetsky-Shapiro, & Smyth, 1996).

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.