Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm
| Autor: | Kyounga Park, Ioannis Kaparias, Heidrun Belzner, Michael G.H. Bell |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Fuzzy classification
Discretization Neuro-fuzzy Computer science Decision tree Machine learning computer.software_genre Fuzzy logic Defuzzification Fuzzy transportation Fuzzy number Fuzzy associative matrix Civil and Structural Engineering Adaptive neuro fuzzy inference system Incremental decision tree business.industry Mechanical Engineering Decision tree learning Fuzzy control system Information Fuzzy Networks Fuzzy set operations Artificial intelligence business computer Algorithm Membership function |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2076:20-28 |
| ISSN: | 2169-4052 0361-1981 |
| DOI: | 10.3141/2076-03 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|