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Ant Colony Algorithms for Data Classification
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).
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