Adaptive Parameter Control Strategy for Ant-Miner Classification Algorithm

ABC-Miner: An Ant-Based Bayesian Classification Algorithm

Bayesian networks (BNs) are powerful tools for knowledge representation and inference that encode (in)dependencies among random variables. A Bayesian network classifier is a special kind of these networks that aims to compute the posterior probability of each class given an instance of the attributes and predicts the class with the highest posterior probability. Since learning the optimal BN st...

Extensions to the Ant-Miner Classification Rule Discovery Algorithm

Data classification by Fuzzy Ant-Miner

In this paper we propose an extension of classification algorithm based on ant colony algorithms to handle continuous valued attributes using the concepts of fuzzy logic. The ant colony algorithms transform continuous attributes into nominal attributes by creating clenched discrete intervals. This may lead to false predictions of the target attribute, especially if the attribute value history i...

cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes

This paper presents an extension to Ant-Miner, named cAntMiner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes “on-the-fly”, cAnt-Miner does not requires a discretization method in ...

Extending the ABC-Miner Bayesian Classification Algorithm

ABC-Miner is a Bayesian classification algorithm based on the Ant Colony Optimization (ACO) meta-heuristic. The algorithm learns Bayesian network Augmented Näıve-Bayes (BAN) classifiers, where the class node is the parent of all the nodes representing the input variables. However, this assumes the existence of a dependency relationship between the class variable and all the input variables, and...