Evolutionary semi-supervised learning with swarm intelligence

Ping He, Lin Lu, Xiao-hua Xu, Heng Qian, Wei Zhang, Yongsheng Ju

Abstract: To address the issue of evolutionary data classification, we propose an evolving swarm classification model. It treats each class as an ant colony carrying different type of pheromone. The ant colonies send their members to propagate their unique pheromone on the unlabeled instances, so as to label them for member recruitment. Meanwhile, the unlabeled instances are treated as unlabeled ants, which also have their preferences for joining one of those labeled colonies. We call it homing feedback, and integrate it into the pheromone update process. Afterwards, the natural selection process is carried out to keep a balance between the member recruitment and the ant colony size maintenance. Sufficient experiments demonstrate that our algorithm is effective in the real-world evolutionary classification applications.