An Adaptive Hybrid Ant Colony Optimization Algorithm for The Classification Problem

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.

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

“…Jero et al (2016) applied ACOR to Electrocardiography steganography. Ma et al (2019) proposed an adaptive hybrid ACO for the classification problem. Omran and Al-Sharhan (2019) applied two variants of his proposed ACOR to practical engineering optimization problems.…”

Research on laser confocal free-form surface measurement method based on adaptive ant colony algorithm

“…Research also proposed alignment strategies based on game theory methods, strategies based on genetic algorithms (GA), particle swarm optimization (PSO), etc. However, these methods still have problems such as noise sensitivity and low accuracy [9][10][11][12][13][14] .…”