ID3算法
模糊逻辑
决策树
计算机科学
去模糊化
模糊分类
机器学习
增量决策树
模糊集运算
人工智能
数据挖掘
决策树学习
神经模糊
模糊数
算法
模糊集
模糊控制系统
作者
Kyounga Park,Michael G.H. Bell,Ioannis Kaparias,Heidrun Belzner
摘要
This study introduces a way to overcome the sensitivity of decision trees used for route choice behavior studies by using fuzzy logic while preserving the advantages of decision trees and the C4.5 algorithm, namely, comprehensibility and ease of application. Soft discretization of continuous values in fuzzy decision trees can provide a more robust classification. Also, the use of fuzzy logic makes it possible to accommodate qualitative attributes describing route characteristics. Apart from these features, fuzzy decision tree learning algorithms are also capable of assigning numeric values on decisions about the degree of certainty of each recommendation emanating from the fuzzy reasoning. This feature makes it possible to solve the multiple suggestion problem whereby the classic decision tree may suggest that more than one route is optimal. To investigate improvements resulting from the application of fuzzy decision tree learning algorithms, software for an adaptive route choice model using a fuzzy decision tree learning algorithm, fuzzy ID3, was developed and simulation experiments with the model were carried out. The comparison of results with the nonfuzzy adaptive route choice model indicates better predictive accuracy and more effective applicability for the fuzzy model in practice.
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