A Q-learning-based multi-agent system for data classification

作者：Pourpanah Farhad; Tan Choo Jun; Lim Chee Peng^*; Mohamad Saleh Junita

来源：Applied Soft Computing, 2017, 52: 519-531.

DOI：10.1016/j.asoc.2016.10.016

• Fuzzy ARTMAP
• Multi-agent system
• Q-learning
• Trust measurement
• Data classification

In this paper, a multi-agent classifier system with Q-learning is proposed for tackling data classification problems. A trust measurement using a combination of Q-learning and Bayesian formalism is formulated. Specifically, a number of learning agents comprising hybrid neural networks with Q-learning, which we have formulated in our previous work, are devised to form the proposed Q-learning Multi-Agent Classifier System (QMACS). The time complexity of QMACS is analyzed using the big O-notation method. In addition, a number of benchmark problems are employed to evaluate the effectiveness of QMACS, which include small and large data sets with and without noise. To analyze the QMACS performance statistically, the bootstrap method with 95% confidence interval is used. The results from QMACS are compared with those from its constituents and other models reported in the literature. The outcome indicates the effectiveness of QMACS in combining the predictions from its learning agents to improve the overall classification performance.

• 出版日期2017-3