An Approach to Data Reduction and Integrated Machine Classification

Abstract

The goal of the paper is to propose a novel approach to integrated machine classification and to investigate the effect of integration of the data reduction with data mining stage. The integration of both important steps of knowledge discovery in databases is recognized as a vital step towards improving effectiveness of the data mining effort. After having the introduced data reduction and integration schemes a solution to the integrated classification problem is proposed. The proposed algorithm allows for integrating data reduction through simultaneous instance and feature selection, with learning process using population-based and A-Team techniques. To validate the proposed approach and to investigate the effect of data reduction combined with different integration schemes, the computation experiment has been carried out. Experiment based on several benchmark datasets has shown that integrated data reduction and classifier learning outperform traditional approaches.