Fuzzy decision tree based approach to predict the type of pavement repair

Authors:
Devinder Kaur;Haricharan Pulugurta
Affiliations:
Electrical and Computer Science Engineering, University of Toledo;Civil Engineering, University of Toledo
Venue:
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Year:
2007

Citing 7
Cited 2

C4.5: programs for machine learning

C4.5: programs for machine learning
Induction of fuzzy decision trees

Fuzzy Sets and Systems
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A complete fuzzy decision tree technique

Fuzzy Sets and Systems - Theme: Learning and modeling
Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index: Research Articles

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
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

Comparitive analysis of fuzzy decision tree and logistic regression methods for pavement treatment prediction

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 containing historical pavement information. Generally there are many attributes in the pavement database and often it is a complicated process to develop any mathematical model to classify the data. This paper demonstrates the use of fuzzy logic to generate decision tree to classify the pavement data. The fuzzy decision tree is then converted to fuzzy rules. These fuzzy rules 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. The model was developed and tested using the ODOT (Ohio Department of Transportation) data set.