Pattern classification via linear programming
Proceedings of the 15th annual conference on Computers and industrial engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A case-based distance model for multiple criteria ABC analysis
Computers and Operations Research
Controlling inventory by combining ABC analysis and fuzzy classification
Computers and Industrial Engineering
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Management of multicriteria inventory classification
Mathematical and Computer Modelling: An International Journal
An approach based on ANFIS input selection and modeling for supplier selection problem
Expert Systems with Applications: An International Journal
Performance of classification models from a user perspective
Decision Support Systems
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
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Classification is a procedure to separate data or alternatives into two or more classes. In practice, the need to classify alternatives involving multiple criteria into distinct classes is considerable. Therefore, determining how to assist decision makers in classifying alternatives into multiple classes is an important issue in the field of multiple-criteria decision aids. This study proposes a two-phase case-based distance approach used to assist decision makers to classify alternatives into multiple groups. By incorporating the advantages of the case-based distance method, the proposed two-phase approach can classify alternatives by evaluating a set of cases selected by decision makers, reduce the number of misclassifications, improve multiple solution problems, and lessen the impact of outliers. An interactive classification procedure is also proposed to provide flexibility in such a way that decision makers can check and adjust classification results iteratively.