A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining

Abstract

We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining—where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant.

Using Bio-inspired intelligence for Web opinion Mining

“…Posts are processed in order to: i) Identify and extract users' opinions and ii) automatically weight the users' comments according to the presence or not of arguments justifying their opinion about a certain service, product or governmental decision using the PSO/ACO2 [18,57] algorithm.…”

“…PSO/ACO2, proposed by Holden and Freitas [57] belongs to the family of bio inspired algorithms. It is inspired by Particle Swarm Optimization (PSO) [59] and Ant Colony Optimization (ACO) [60].…”

Uncertainty Reduction in Data mining Task using Fuzzy based Integrated ACO and GA

“…This new meta-heuristic has been shown to be both robust and versatile in the sense that it has been successfully applied to a range of different combinatorial optimization problems [2], [3], [6]. In [1] describe equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms was proposed on the basis of the existing literature. In [9], systems for clustering with collectives of autonomous agents follow either the ant approach of picking up and dropping objects or the DataBot approach of identifying the data points with artificial life creatures.…”

An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

“…A hybrid method that combines Particle Swarm Optimization and Ant Colony Optimization (PSO/ACO) was introduced in (Holden and Freitas, 2008) for mining rules that can be used fr classification. The disadvantage of using PSO algorithm was that nominal values had to be converted into binary numbers before mining.…”