An Adaptive Hybrid Ant Colony Optimization Algorithm for The Classification Problem

  • Anxiang Ma Northeastern University
  • Xiaohong Zhang Northeastern University
  • Changsheng Zhang Northeastern University
  • Bin Zhang Northeastern University

Abstract

Classification is an important data analysis and data mining technique. Taking into account the comprehensibility of the classifier generated, an adaptive hybrid ant colony optimization algorithm called A_HACO is proposed which can effectively solve classification problem and get the comprehensible classification rules at the same time. The algorithm incorporates the artificial bee colony optimization strategy into the ant colony algorithm. The ant colony global optimization process is used to adaptively select the appropriate rule evaluation function for the data set given. Based on the classification rules obtained, the artificial bee colony optimization strategy is used to tackle the continuous attributes for further optimization of classification rules. This approach is evaluated experimentally using different standard real datasets, and compared with some proposed related classification algorithms. It shows that A_HACO can adaptively select the appropriate rule evaluation function and has better accuracy compared with related works.

Author Biographies

Anxiang Ma, Northeastern University
College of Computer Science & Engineering
Xiaohong Zhang, Northeastern University
College of Computer Science & Engineering
Changsheng Zhang, Northeastern University
College of Computer Science & Engineering
Bin Zhang, Northeastern University
College of Computer Science & Engineering
Published
2019-12-18
Section
Articles