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On constructing a fuzzy inference framework using crisp decision trees

机译:基于清晰决策树的模糊推理框架构建

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This paper proposes a framework which consists of a novel fuzzy inference algorithm to generate fuzzy decision trees from induced crisp decision trees. Fuzzy theoretical techniques are used to fuzzify crisp decision trees in order to soften sharp decision boundaries at decision nodes inherent in this type of trees. A framework for the investigation of various types of membership functions and fuzzy inference techniques is proposed. Once the decision tree has been fuzzified, all branches throughout the tree will fire, resulting in a membership grade being generated at each branch. Five different fuzzy inference mechanisms are used to investigate the degree of interaction between membership grades on each path in the decision tree, which ultimately leads to a final crisp classification. A genetic algorithm is used to optimize and automatically determine the set of fuzzy regions for all branches and simultaneously the degree in which the inference parameters will be applied. Comparisons between crisp trees and the fuzzified trees suggest that the later fuzzy tree is significantly more robust and produces a more balanced classification. In addition, the results obtained from five real-world data sets show that there is a significant improvement in the accuracy of the fuzzy trees when compared with crisp trees.
机译:本文提出了一个框架,该框架由新颖的模糊推理算法组成,该算法可以从诱导的清晰决策树生成模糊决策树。模糊理论技术用于模糊处理的决策树,以软化此类树中固有的决策节点处的清晰决策边界。提出了研究各种隶属度函数和模糊推理技术的框架。对决策树进行模糊处理后,整个决策树中的所有分支都会触发,从而在每个分支上生成成员资格等级。五个不同的模糊推理机制用于调查决策树中每个路径上的成员资格等级之间的交互程度,最终导致最终的明晰分类。遗传算法用于优化和自动确定所有分支的模糊区域集,并同时确定推理参数的应用程度。脆树和模糊树之间的比较表明,后面的模糊树明显更健壮,并且产生更平衡的分类。此外,从五个真实世界的数据集获得的结果表明,与清晰树相比,模糊树的准确性有了显着提高。

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