This webpage contains:

• Abstract
• Summary
• Experimental study

Abstract:

Feature and Instance Selection are two effective data reduction processes which can be applied to classification tasks obtaining promising results. Although both processes are defined separately, it is possible to apply them simultaneously.

This paper proposes an evolutionary model to perform Feature and Instance Selection in nearest neighbour classification. It is based on Cooperative Coevolution, which has been applied to many computacional problems with great success.

The proposed approach is compared with a wide range of evolutionary feature and instance selection methods for classification. The results contrasted through nonparametric statistical tests show that our model outperforms previously proposed evolutionary approaches for performing data reduction processes in combination with the nearest neighbour rule.

Summary:

1. Introduction
2. Background:data reduction and coevolutionary algorithms
3. A cooperative coevolutionary algorithm for instance and feature selection: IFS-CoCo
4. Experimental framework
5. Results and analysis
6. Concluding remarks
A. Description of the algorithms employed on the experimental study
B. Full results of the experimental study