基于生成模型的Q-learning二分类算法

Q-learning algorithm based on generative model for binary classification

对于二分类问题,基于判别模型的分类器一般都是寻找一条最优判决边界,容易受到数据波动的影响。针对该问题提出一种基于生成模型的Q-learning二分类算法(BGQ-learning),将状态和动作分开编码,得到对应各类的判决函数,增加了决策空间的灵活性,同时在求解参数时,采用最小二乘时序差分(TD)算法和半梯度下降法的组合优化方法,加速了参数的收敛速度。设计实验对比了BGQ-learning算法与三种经典分类器以及一种新颖的分类器的分类性能,在UCI数据库七个数据集上的测试结果表明,该算法有着优良的稳定性以及良好的分类精确度。

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.