The Ant Colony Optimization (ACO) algorithms have been well‐studied by the Operations
Research community for solving combinatorial optimization problems. A handful of …

Irrelevant and redundant features increase the computation and storage requirements, and
the extraction of required information becomes challenging. Feature selection enables us to …

Bayesian networks are knowledge representation tools that model the (in) dependency
relationships among variables for probabilistic reasoning. Classification with Bayesian …

Abstract Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers
that consist of several local Bayesian networks, one for each predictable class, to model an …

While many papers propose innovative methods for constructing individual rules in separate-
and-conquer rule learning algorithms, comparatively few study the heuristic rule evaluation …

Bayesian multi-net (BMN) classifiers consist of several local models, one for each data
subset, to model asymmetric, more consistent dependency relationships among variables in …

Abstract Ant Colony Optimisation (ACO) has been successfully applied to the classification
task of data mining in the form of Ant-Miner. A new extension of Ant-Miner, called c Ant-Miner …

Ant colony optimization (ACO) algorithms for classification in general employ a sequential
covering strategy to create a list of classification rules. A key component in this strategy is the …

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony
Optimization (ACO) metaheuristic. cAnt-Miner is the extended version of the algorithm that …

In this paper we adopt a cooperative strategy based on ACO (Ant Colony Optimization)
algorithms to coordinate a Multi Robots System (MRS). Our principal objective is to evaluate …