Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery

Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery

The cAnt-Miner algorithm is an Ant Colony Optimization (ACO) based technique for classification rule discovery in problem domains which include continuous attributes. In this paper, we propose several extensions to cAntMiner. The main extension is based on the use of multiple pheromone types, one for each class value to be predicted. In the proposed μcAnt-Miner algorithm, an ant first selects a...

An Ant Colony Algorithm for Classification Rule Discovery

This work proposes an algorithm for rule discovery called Ant-Miner (Ant Colony-based Data Miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is based on recent research on the behavior of real ant colonies as well as in some data mining concepts. We compare the performance of Ant-Miner with the performance of the well-known C4.5 algorithm on six public do...

Classification-Rule Discovery with an Ant Colony Algorithm

Ant colony optimization (ACO) is a relatively new computational intelligence paradigm inspired by the behaviour of natural ants (Bonabeau, Dorigo & Theraulaz, 1999). The natural behaviour of ants that we are interested in is the following. 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 w...

Extensions to the Ant-Miner Classification Rule Discovery Algorithm

Classification Rule Discovery with Ant Colony Optimization

In [9], we presented a modified version of Ant-Miner (i.e. Ant-Miner2), where the core computation heuristic value was based on a simple density estimation heuristic. In this paper, we present a further study and introduce another ant-based algorithm, which uses a different pheromone updating strategy and state transition rule. By comparison with the work of Parpinelli et al, our method can imp...