Optimal Feature Subset Selection using Ant Colony Optimization

Feature Subset Selection Using Ant Colony Optimization

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes...

Ant Colony Optimization for Feature Subset Selection

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that ut...

Ant Colony Optimization Toward Feature Selection

A hybrid approach for feature subset selection using neural networks and ant colony optimization

One of the significant research problems in multivariate analysis is the selection of a subset of input variables that can predict the desired output with an acceptable level of accuracy. This goal is attained through the elimination of the variables that produce noise or, are strictly correlated with other already selected variables. Feature subset selection (selection of the input variables) ...

Feature Selection Technique Using Ant Colony Optimization on Keystroke Dynamics

The work is concerned with the use of Ant Colony Optimization algorithm for feature selection of Keystrokes Dynamics and comparison of classification accuracy of Multi-SVM and KNN classifiers. There are various approaches used for feature subset selection but, ACO algorithm gives good performance than other feature selection algorithm like Genetic Based algorithm and Particle Swarm Optimization...