基于混淆矩阵的多目标优化三支决策模型

Three-Way Decisions Model for Multi-object Optimization Based on Confusion Matrix

鉴于混淆矩阵在机器学习算法性能评价领域的通用性,文中以混淆矩阵为基础构造概率粗糙集三支决策度量系统,给出部分度量指标之间的性质及其证明,提出基于混淆矩阵度量指标体系的多目标优化三支决策阈值求解模型.模型中多目标优化函数被视为不同三支决策度量指标的加权之和,而最优阈值的求解也获得一种新型的语义解释.最后通过实例演示模型如何确定接受与拒绝域阈值,同时对比Pawlak粗糙集方法,表明文中模型获得的三支决策能够更好地平衡决策的准确率与承诺率.

In consideration of the generalized application of confusion matrix as an important algorithmic measurement tool in machine learning field, a three-way decision measure system of the probabilistic rough set is constructed based on three-way decision confusion matrix. Then, the properties of partial three-way decision measures are discussed. A multi-object optimization function model for three-way decisions thresholds computing is proposed as well. In this model, multi-object optimization functions are considered as weighted sums of three-way decisions measures , and a new semantic interpretation is acquired for solving the optimal threshold. Finally, the solving process of accepting and rejecting thresholds of the model is demonstrated via an case. By comparing with the classic Pawlak rough set method and confusion matrix model, the confusion matrix model can better balance the accurate rate and the commitment rate for three-way decisions.