Ant-Based Feature and Instance Selection for Multiclass Imbalanced Data

Abstract

This paper introduces a novel algorithm called Ant-based Feature and Instance Selection. This new algorithm addresses the simultaneous selection of instances and features for mixed, incomplete, and imbalanced data in the context of lazy instance-based classifiers. The proposed algorithm uses a hybrid selection strategy based on metaheuristic procedures and Rough Sets. The Ant-based Feature and Instance Selection algorithm combines Ant Colony Optimization and Generic Extended Rough Sets for Mixed and Incomplete Information Systems. It has five stages: reduct computation, metadata computation, intelligent instance preprocessing, submatrices creation, and fusion. To test the performance of the proposed algorithm, we used 25 datasets from the Machine Learning repository of the University of California at Irvine. All these datasets are imbalanced, with multiple classes and represent real-world classification problems. The number of classes ranges between three and eight classes. Most of them also have mixed or incomplete descriptions. We used several performance measures and computed the Instance Retention ratio and the Feature Retention ratio. To determine the existence or not of significant differences in the performance of the compared algorithms, we used non-parametric hypothesis testing. The statistical analysis results confirm the high quality of the proposed algorithm for selecting features and instances in multiclass imbalanced data.

Keywords

• Multiclass imbalanced data
• feature selection
• instance selection
• nearest neighbor