MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network
Applied Intelligence
A New Complicated-Knowledge Representation Approach Based on Knowledge Meshes
IEEE Transactions on Knowledge and Data Engineering
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications: An International Journal
Information Systems Frontiers
Feature-Weighted CBR with neural network for symbolic features
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Hybrid system of case-based reasoning and neural network for symbolic features
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
ICWE'05 Proceedings of the 5th international conference on Web Engineering
Intelligent measuring and improving model for customer satisfaction level in e-government
EGOV'05 Proceedings of the 4th international conference on Electronic Government
Developing an intelligent supplier chain system collaborating with customer relationship management
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Hi-index | 0.00 |
We propose a hybrid prediction system of neural network and memory-based learning. Neural network (NN) and memory-based reasoning (MBR) are frequently applied to data mining with various objectives. They have common advantages over other learning strategies. NN and MBR can be directly applied to classification and regression without additional transformation mechanisms. They also have strength in learning the dynamic behavior of the system over a period of time. Unfortunately, they have shortcomings when applied to data mining tasks. Though the neural network is considered as one of the most powerful and universal predictors, the knowledge representation of NN is unreadable to humans, and this “black box” property restricts the application of NN to data mining problems, which require proper explanations for the prediction. On the other hand, MBR suffers from the feature-weighting problem. When MBR measures the distance between cases, some input features should be treated as more important than other features. Feature weighting should be executed prior to prediction in order to provide the information on the feature importance. In our hybrid system of NN and MBR, the feature weight set, which is calculated from the trained neural network, plays the core role in connecting both learning strategies, and the explanation for prediction can be given by obtaining and presenting the most similar examples from the case base. Moreover, the proposed system has advantages in the typical data mining problems such as scalability to large datasets, high dimensions, and adaptability to dynamic situations. Experimental results show that the hybrid system has a high potential in solving data mining problems