Incremental Iterative Retrieval and Browsingfor Efficient Conversational CBR Systems
Applied Intelligence
Conversational Case-Based Reasoning
Applied Intelligence
Interactive Case-Based Reasoning in Sequential Diagnosis
Applied Intelligence
A Case-Based Framework for Interactive Capture and Reuse of Design Knowledge
Applied Intelligence
An Interactive Visualisation Tool for Case-Based Reasoners
Applied Intelligence
Supporting Dialogue Inferencing in Conversational Case-Based Reasoning
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
A Web based Conversational Case-Based Recommender System for Ontology aided Metadata Discovery
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Decision support in the railway accident rescue by hybrid reasoning
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Hi-index | 12.05 |
Case-based reasoning (CBR) models often solve problems by retrieving multiple previous cases and integrating those results. However, conventional CBR makes decisions by comparing the integrated result with the cut-off point irrespective of the degree of the adjacency between them. This can cause increasing misclassification error for the target cases adjacent to the cut-off point, since the results of previous cases used to produce those results are relatively inconsistent with each other. In this article, we suggest a new interactive CBR model called grey-zone case-based reasoning (GCBR) that makes decisions focusing additional attention on the cases near the cut-off point by interactive communication with users. GCBR classifies results automatically for the cases placed outside the cut-off point boundary area. On the other hand, it communicates with users to make decision for the cases placed inside the area by verifying characteristics of the dataset. We suggest the architecture of GCBR and implement its prototype.