Machine Learning
Data mining: concepts and techniques
Data mining: concepts and techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Rule-based and case-based reasoning approach for internal audit of bank
Knowledge-Based Systems
SOPHIA-TCBR: A knowledge discovery framework for textual case-based reasoning
Knowledge-Based Systems
Quality management in GPRS networks with fuzzy case-based reasoning
Knowledge-Based Systems
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
Two-step filtering datamining method integrating case-based reasoning and rule induction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
A case-based reasoning safety decision-support tool: Nextcase/safety
Expert Systems with Applications: An International Journal
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
General competence trust among supply chain partners, referring to the trust that a partner holds the general ability of fulfilling contracts, is a critical factor to ensure effective cooperation in a supply chain, especially in the current financial crisis. The method of supply chain trust diagnosis (SCTD) is to evaluate whether or not a partner holds such competence. This research devotes to an early investigation on diagnosing competence trust of supply chain with the method of inductive case-based reasoning ensemble (ICBRE). The so-called supply chain trust diagnosis with inductive case-based reasoning ensemble consists of five levels, that is, information level, the level of ratios of general competence states, the level of inductive case-based reasoning, ensemble level, and diagnosis result level. Knowledge for diagnosing competence trust, which composes of a case base, is hidden in data represented by ratios of general competence states. Inductive approach is combined with randomness to construct diverse and good member methods of inductive case-based reasoning. Finally, simple voting is used to integrate outputs of member inductive case-based reasoning methods in order to produce the final diagnosis on whether or not a partner holds the general ability of fulfilling contracts. We statistically validated results of the method of supply chain trust diagnosis with inductive case-based reasoning ensemble by comparing them with those of multivariate discriminant analysis, logistic regression, single Euclidean case-based reasoning, and single inductive case-based reasoning. The results indicate that the method of supply chain trust diagnosis with inductive case-based reasoning ensemble significantly improves predictive capability of case-based reasoning in this problem and outperforms all the comparative models by group decision of several decision-making agents and non-strict assumptions like statistical methods.