Paper Info
Title
Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm.
Abstract
Abstract Ant-Miner is an ant-based algorithm for the discovery of classification rules. This paper proposes five extensions to Ant-Miner: (1) we utilize multiple types of pheromone, one for each permitted rule class, i.e. an ant first selects the rule class and then deposits the corresponding type of pheromone; (2) we use a quality contrast intensifier to magnify the reward of high-quality rules and to penalize low-quality rules in terms of pheromone update; (3) we allow the use of a logical negation operator in the antecedents of constructed rules; (4) we incorporate stubborn ants, an ACO variation in which an ant is allowed to take into consideration its own personal past history; (5) we use an ant colony behavior in which each ant is allowed to have its own values of the α and β parameters (in a sense, to have its own personality). Empirical results on 23 datasets show improvements in the algorithm’s performance in terms of predictive accuracy and simplicity of the generated rule set.
Year
DOI
Venue
2011
10.1007/s11721-011-0057-9
Swarm intelligence
Keywords
Field
DocType
Ant Colony Optimization (ACO),Data mining,Classification,Multipheromone,Stubborn Ants
ANT,Classification rule,Negation,Computer science,Algorithm,Pheromone,Operator (computer programming),Artificial intelligence,Ant colony,Machine learning,Computer programming
Journal
Volume
Issue
ISSN
5
3-4
1935-3820
Citations 
PageRank 
References 
25
0.85
17
Authors
3
Name
Order
Citations
PageRank
Khalid M. Salama116013.09
Ashraf M. Abdelbar224325.43
J A Foster388481.48