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
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
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Cytological Breast Fine Needle Aspirate Images Analysis with a Genetic Fuzzy Finite State Machine
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Knowledge and Information Systems
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
Case-based estimation of the risk of enterobiasis
Artificial Intelligence in Medicine
Recognizing yield patterns through hybrid applications of machine learning techniques
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Multi-modal and multi-purpose case-based reasoning in the health sciences
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Case-based systems in health sciences: a case study in the field of stress
WSEAS TRANSACTIONS on SYSTEMS
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Combinations of case-based reasoning with other intelligent methods
International Journal of Hybrid Intelligent Systems - CIMA-08
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
Research on CBR system based on data mining
Applied Soft Computing
A case retrieval approach using similarity and association knowledge
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Retrieval in CBR using a combination of similarity and association knowledge
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Journal of Intelligent Manufacturing
Computer Methods and Programs in Biomedicine
Improving user experience with case-based reasoning systems using text mining and Web 2.0
Expert Systems with Applications: An International Journal
Automated Diagnosis Through Ontologies and Logical Descriptions: The ADONIS Approach
International Journal of Decision Support System Technology
An intelligent route management system for electric vehicle charging
Integrated Computer-Aided Engineering
Developing a hybrid predictive system for retinopathy
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 12.06 |
Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.