scholarly journals MODAL CHOICE ANALYSIS WITH FUZZY DECISION TREE APPROACH

2007 ◽  
Vol 63 (2) ◽  
pp. 145-157
Author(s):  
Takamasa AKIYAMA ◽  
Masashi OKUSHIMA
Keyword(s):  
Decision Tree ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Modal Choice ◽  
Choice Analysis ◽  
Tree Approach

Semantic Provenance Based Trustworthy Users Classification on Book-Based Social Network using Fuzzy Decision Tree

2020 ◽  
Vol 28 (01) ◽  
pp. 47-77
Author(s):  
Dhanalakshmi Teekaraman ◽  
S. Sendhilkumar ◽  
G. S. Mahalakshmi
Keyword(s):  
Social Network ◽  
Decision Tree ◽  
Trust Model ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Time Data ◽  
User Classification ◽  
Proposed Model ◽  
Ontological Model ◽  
Tree Approach

As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.

Fuzzy Decision Tree Approach for Optimal Supplier Base

2013 ◽  
Vol 315 ◽  
pp. 283-287
Author(s):  
Hock Kheng Sim ◽  
Zhi Xin Wong
Keyword(s):  
Decision Tree ◽  
Optimal Number ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Linguistic Terms ◽  
Decision Tree Method ◽  
Supplier Risk ◽  
Production Decision ◽  
Tree Approach ◽  
Tree Method

Minimizing supplier base allows better supplier relationship management, but any supplier failure will interrupt the supplies chain. Earth quake in Fukushima, Japan and flood in Bangkok, Thailand, caused supply chain breakdown and affect the global production. Decision tree method is recommended to determine optimal number of supplier in supplier base with the risk of supplier failure. This paper presented an approach to integrate fuzzy logic in decision tree approach by rating the supplier risk in linguistic terms for imperfect environment. Numerical example and sensitivity are carried out and presented, and optimal supplier base was identified.

Partial Discharge Pattern Classification Using the Fuzzy Decision Tree Approach

2005 ◽  
Vol 54 (6) ◽  
pp. 2258-2263 ◽  
Author(s):  
T.K. Abdel-Galil ◽  
R.M. Sharkawy ◽  
M.M.A. Salama ◽  
R. Bartnikas
Keyword(s):  
Decision Tree ◽  
Pattern Classification ◽  
Partial Discharge ◽  
Discharge Pattern ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Approach

A survey on geographic classification of virgin olive oil with using T-operators in Fuzzy Decision Tree Approach

2016 ◽  
Vol 155 ◽  
pp. 86-96 ◽  
Author(s):  
Efendi Nasibov ◽  
Suzan Kantarcı Savaş ◽  
Alper Vahaplar ◽  
Ayşe Övgü Kınay
Keyword(s):  
Decision Tree ◽  
Olive Oil ◽  
Virgin Olive Oil ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Approach
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