Soft computing in case based reasoning
Towards integration of memory based learning and neural networks
Soft computing in case based reasoning
Experiments On Adaptation-Guided Retrieval In Case-Based Design
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Relational Case-based Reasoning for Carcinogenic Activity Prediction
Artificial Intelligence Review
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge management for computational intelligence systems
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Architectural overview of the computational intelligence testing tool
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Systematically evolving configuration parameters for computational intelligence methods
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Systematically evolving configuration parameters for computational intelligence methods
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
| Hi-index | 0.00 |
This paper describes how CBR can be used to compare, reuse, and adapt inductive models that represent complex systems. Complex systems are not well understood and therefore require models for their manipulation and understanding. We propose an approach to address the challenges for using CBR in this context, which relate to finding similar inductive models (solutions) to represent similar complex systems (problems). The purpose is to improve the modeling task by considering the quality of different models to represent a system based on the similarity to a system that was successfully modeled. The revised and confirmed suitability of a model can become additional evidence of similarity between two complex systems, resulting in an increased understanding of a domain. This use of CBR supports tasks (e.g., diagnosis, prediction) that inductive or mathematical models alone cannot perform. We validate our approach by modeling software systems, and illustrate its potential significance for biological systems.