Ant Colony Optimization
On the quest for optimal rule learning heuristics
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
A study of different quality evaluation functions in the cAnt-Miner(PB) classification algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
ABC-miner: an ant-based bayesian classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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Learning classifiers from datasets is a central problem in data mining and machine learning research. ABC-Miner is an Ant-based Bayesian Classification algorithm that employs the Ant Colony Optimization (ACO) meta-heuristics to learn the structure of Bayesian Augmented Naïve-Bayes (BAN) Classifiers. One of the most important aspects of the ACO algorithm is the choice of the quality measure used to evaluate a candidate solution to update pheromone. In this paper, we explore the use of various classification quality measures for evaluating the BAN classifiers constructed by the ants. The aim is to discover how the use of different evaluation measures affects the quality of the output classifier in terms of predictive accuracy. In our experiments, we use 4 different classification measures on 15 benchmark datasets.