Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Soft computing in case based reasoning
Soft computing in case based reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
A hybrid approach of neural network and memory-based learning to data mining
IEEE Transactions on Neural Networks
Applying fuzzy neural network to estimate software development effort
Applied Intelligence
Hybrid model for learner modelling and feedback prioritisation in exploratory learning
International Journal of Hybrid Intelligent Systems - CIMA-08
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
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
Feature-Weighted CBR with neural network for symbolic features
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
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Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.