A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
Selection of the optimal prototype subset for 1-NN classification
Pattern Recognition Letters
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Expert Systems with Applications: An International Journal
Application of support vector machines to corporate credit rating prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
Global optimization of case-based reasoning for breast cytology diagnosis
Expert Systems with Applications: An International Journal
Applying case-based reasoning for product configuration in mass customization environments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A case-based reasoning model that uses preference theory functions for credit scoring
Expert Systems with Applications: An International Journal
Geographic knowledge discovery from Web Map segmentation through generalized Voronoi diagrams
Expert Systems with Applications: An International Journal
A unified framework for market segmentation and its applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Case-based reasoning system (CBR) has been widely applied to the issue of market segmentation. Most of previous studies focused on dividing customers into two groups. Consequently, traditional voting method used for two groups in CBR would become inappropriate when one would like to divide customers into three groups through some segmentation variable. In this paper, a new voting method called 3NN+1 is proposed to bridge the gap. To make the inference of the 3NN+1 based CBR system more efficient, the features and instances (or cases) for reasoning is selected simultaneously by means of genetic algorithms. This new system is applied to a real case of notebook market to demonstrate its usefulness for market segmentation. From the results of the real case, it shows that the system would be valuable to enterprises, when dividing customers into three groups in compliance with their purchasing behaviors for developing marketing strategies.