A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
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
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Selecting representative examples and attributes by a genetic algorithm
Intelligent Data Analysis
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Selecting Features and Objects for Mixed and Incomplete Data
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Web-based CBR system applied to early cost budgeting for pavement maintenance project
Expert Systems with Applications: An International Journal
Majority voting combination of multiple case-based reasoning for financial distress prediction
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evaluating case selection algorithms for analogical reasoning systems
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Case-based reasoning support for liver disease diagnosis
Artificial Intelligence in Medicine
Research on CBR system based on data mining
Applied Soft Computing
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
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
Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction
Information Sciences: an International Journal
Intelligent feature and instance selection to improve nearest neighbor classifiers
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Literature review on the creativity of CBR applications
Artificial Intelligence Review
Hi-index | 12.06 |
Many studies have tried to optimize parameters of case-based reasoning (CBR) systems. Among them, selection of appropriate features to measure similarity between the input and stored cases more precisely, and selection of appropriate instances to eliminate noises which distort prediction have been popular. However, these approaches have been applied independently although their simultaneous optimization may improve the prediction performance synergetically. This study proposes a case-based reasoning system with the two-dimensional reduction technique. In this study, vertical and horizontal dimensions of the research data are reduced through our research model, the hybrid feature and instance selection process using genetic algorithms. We apply the proposed model to a case involving real-world customer classification which predicts customers' buying behavior for a specific product using their demographic characteristics. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of the typical CBR system.