Hybrid system of case-based reasoning and neural network for symbolic features

Authors:
Kwang Hyuk Im;Tae Hyun Kim;Sang Chan Park
Affiliations:
Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea;Department of Industrial Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
Venue:
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Year:
2005

Citing 8
Cited 2

Toward memory-based reasoning

Communications of the ACM - Special issue on parallelism
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features

Machine Learning
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review and Empirical Evaluation of Feature Weighting Methods for aClass of Lazy Learning Algorithms

Artificial Intelligence Review - Special issue on lazy learning
Designing the User Interface: Strategies for Effective Human-Computer Interaction

Designing the User Interface: Strategies for Effective Human-Computer Interaction
Weighting Features

ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Improved heterogeneous distance functions

Journal of Artificial Intelligence Research
A hybrid approach of neural network and memory-based learning to data mining

IEEE Transactions on Neural Networks

A personalized counseling system using case-based reasoning with neural symbolic feature weighting (CANSY)

Applied Intelligence
Feature-Weighted CBR with neural network for symbolic features

ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Case-based reasoning is one of the most frequently used tools in data mining. Though it has been proved to be useful in many problems, it is noted to have shortcomings such as feature weighting problems. In previous research, we proposed a hybrid system of case-based reasoning and neural network. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains. We propose another hybrid system of case-based reasoning and neural network, which uses value difference metric (VDM) for symbolic features. The proposed system is validated by datasets in symbolic domains.