Semantic database modeling: survey, applications, and research issues
ACM Computing Surveys (CSUR)
Concept learning and heuristic classification in weak-theory domains
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
A hybrid architecture for fuzzy connectionist expert systems
Hybrid architectures for intelligent systems
Models and guidelines for integrating expert systems and neural networks
Hybrid architectures for intelligent systems
Case-based reasoning
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Inside Case-Based Reasoning
Machine Learning
The Combinatorial Neural Network: A Connectionist Model for Knowledge Based Systems
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
PBL: Prototype-Based Learning Algorithms
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
A Classification System for Credit Card Transactions
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Prototype of an Intelligent Failure Analysis System
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Dynamic scripting in crisis environments
UI-HCII'07 Proceedings of the 2nd international conference on Usability and internationalization
International Journal of Knowledge Engineering and Soft Data Paradigms
| Hi-index | 0.00 |
This paper introduces ChartD_2, a Congenital Heart Disease Diagnostician that employs a case-based model where specific and general knowledge are combined in reasoning. Specific knowledge is represented in the form of cases while general knowledge is represented in the form of category descriptors. When solving a new case, ChartD_2 uses its general knowledge to draw hypotheses and to guide the search for the most similar cases it hasalready ““seen””. The retrieved cases, representing specificknowledge, are then used to support one of the hypotheses and tojustify the conclusion reached. ChartD_2 has been based onan earlier hybrid connectionist/symbolic program called Hycones,developed in the same application domain. Besides enhancing some ofHycones‘ capabilities, the new system proposes solutions for commonproblems in Case-Based Reasoning (CBR), such as case matching,indexing and learning. The system ChartD_2 is presented andevaluated, using real cases collected from a medical database. Theperformance of the system is contrasted with that of Hycones and twoother learning algorithms. Moreover, similar research efforts on theuse of other sources of knowledge by CBR systems are discussed, andtopics for further research are suggested.