C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Machine Learning
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
International Journal of Intelligent Systems in Accounting and Finance Management
Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem
Intelligent information processing II
SPSS 14.0 Advanced Statistical Procedures Companion
SPSS 14.0 Advanced Statistical Procedures Companion
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Fuzzy decision tree based approach to predict the type of pavement repair
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Soft computing technique in prediction of pavement condition
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An effective sampling method for decision trees considering comprehensibility and accuracy
WSEAS Transactions on Computers
WSEAS Transactions on Information Science and Applications
Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic
Expert Systems with Applications: An International Journal
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Data mining is the process of extraction of hidden predictive information from large databases and expressing them in a simple and meaningful manner. This paper explains the use of Fuzzy logic as a data mining process to generate decision trees from a pavement (road) database obtained from Ohio Department of Transportation containing historical pavement information from 1985 to 2006. Generally there are many attributes in the pavement database and often it is a complicated process to develop a mathematical model to classify the data. This study demonstrates the use of fuzzy logic to generate decision tree to classify the pavement data. Further, the fuzzy decision tree is then converted to fuzzy rules. These fuzzy rules will assist decision-making process for selecting a particular type of repair on a pavement based on its current condition. The fuzzy decision tree induction method used is based on minimizing the measure of classification ambiguity for different attributes. These models overcome the sharp boundary problems, providing soft controller surface and good accuracy dealing with continuous attributes and prediction problems. This method was compared with common logistic regression model for predicting the pavement treatment. The results show that the fuzzy decision method outperforms the logistic regression model by 10%. The fuzzy decision tree method generates the rules, which gives the better understanding of the relationship between the parameters and the pavement treatment prediction.