Bat algorithm (BA) has the advantage of fast convergence, but there is still room for
improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm …

Abstract Structure learning is a very important problem in the field of Bayesian networks
(BNs). It is also an active research area for more than 2 decades; therefore, many …

Ant colony optimization (ACO) has been successfully applied to classification, where the aim
is to build a model that captures the relationships between the input attributes and the target …

Classification is a supervised learning task where a training set is used to construct a classifi-
cation model, which is then used to predict the class of unforeseen test instances. It is often …

A re-occurring challenge in applying feed-forward neural networks to a new dataset is the
need to manually tune the neural network topology. If one's attention is restricted to fully …

ACO R is a well-established ant colony optimization algorithm for continuous-domain
optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's …

In the field of data mining, classification is a supervised learning task whose purpose is to
induce models (classifiers), using a set of labeled training data instances, to predict the class …

Learning Automata (LA) is a popular decision making mechanism to “determine the optimal
action out of a set of allowable actions”(Agache and Oommen, IEEE Trans Syst Man Cybern …

Instance-based learning (IBL) methods predict the class label of a new instance based
directly on the distance between the new unlabeled instance and each labeled instance in …

Classification is a central problem in the fields of data mining and machine learning. Using a
training set of labelled instances, the task is to build a model (classifier) that can be used to …