基于生成模型的 Q-learning二分类算法.

For binary classification problems, the classifier based on the discriminant model usually searches for an optimal decision boundary, which is susceptible to data fluctuations. This paper proposed a Q-learning algorithm based on the generative model for binary classification ( BGQ-learning), which coded state and action separately and obtained corresponding decision functions, increasing the flexibility of decision space. And then it combined least squares temporal-difference (TD) algorithm and semi-gradient descent for parameter optimization, accelerating parameter convergence speed. This paper designed experiments to compare the performance of the proposed algorithm with three classical classifiers and a novel classifier. The test results on 7 data sets of the UCI database show that the proposed algorithm has excellent stability and classification accuracy. [ABSTRACT FROM AUTHOR]