A Quantized Pheromone Ant Colony Optimization Algorithm for Feature Selection

doi:10.12068/j.issn.1005-3026.2020.01.004

Abstract

Abstract: Ant colony optimization algorithms have positive feedback mechanisms and strong searching abilities， which makes them widely used in various kinds of optimization problems. An ant colony optimization algorithm was applied to the field of feature selection and a new quantized pheromone ant colony optimization(QPACO) feature selection algorithm was proposed. Quantum pheromone heuristic strategy was adopted in QPACO algorithm， compared with other ant colony optimization algorithms for feature selection， QPACO algorithm changes the traditional pheromone updating strategy and avoids the local optimization problem when searching for features. In the experimental stage， a KNN classifier was used to guide the learning process， and multiple data sets from the UCI database were used for testing. The experimental results showed that QPACO algorithm has good performances in classification accuracy， precision， recall and feature-reduction.

Key words: feature selection, ant colony optimization, pheromone, quantization, heuristic strategy

CLC Number:

TP18

LI Zhan-shan， LIU Zhao-geng， YU Yin， YAN Wen-hao. A Quantized Pheromone Ant Colony Optimization Algorithm for Feature Selection[J]. Journal of Northeastern University Natural Science, 2020, 41(1): 17-22.

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