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中文摘要：
在三枝决策粗糙集模型中,基于贝叶斯决策理论,在给定的损失函数基础上可以计算出不同决策之间的阈值,从而可以推导出各种现有的概率型粗糙集模型,如可变精度粗糙集模型等.但是决策粗糙集模型需要对损失函数预先设定,这就需要合适的先验知识.本文通过研究三枝决策粗糙集模型中的风险损失和建立模型需要的阈值参数之间的关系,提出了一个最优化问题,给出了理论分析,说明解决该优化问题即可求得所需参数,并给出了一种自适应求阈值参数的算法.该算法将每个样本的条件概率作为搜索空间,以决策风险损失最小化为目标,求得的损失函数和阈值能够使得用户基于此作出的风险最小.在部分数据集上的实验也表明了算法的有效性,利用学习到的阈值建立的三枝决策粗糙集模型能够取得更好的分类性能.
英文摘要：
Three-way decision-theoretic rough set model is a probabilistic extension of the algebraic rough set model.The required parameters for defining probabilistic lower and upper approximations are calculated based on cost functions through Bayesian decision procedure.Through providing different cost functions,decision-theoretic rough set model can derive many other probabilistic rough set models,such as variable precision rough set model,etc.This paper constructs an optimum problem based on decision-theoretic rough set model.Through solving the optimum problem,one can get the proper cost functions and thresholds without any preliminary knowledge.An adaptive learning parameters algorithm is also proposed to solve the optimum problem.The search space of the algorithm is the set of all instances＇probabilities.Under the three-way decision-theoretic rough set model which is based on the learned cost functions and thresholds,the decision cost is minimal and a better classification performance can be gotten from that.The experimental result on some data sets shows the efficiency of our algorithm.
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Hybrid Designing of a Neural System by Combining Fuzzy Logical Framework and PSVM for Visual Haze-Fr
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