Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Expert systems: knowledge, uncertainty, and decision
Expert systems: knowledge, uncertainty, and decision
Exemplar based knowledge acquisition: a unified approach to concept representati on, classification, and learning
Concept learning and heuristic classification in weak-theory domains
Artificial Intelligence
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Object-oriented databases
A knowledge-intensive, integrated approach to problem solving and sustained learning
A knowledge-intensive, integrated approach to problem solving and sustained learning
Modelling class hierarchies in the fuzzy object-oriented data model
Fuzzy Sets and Systems
Case-based reasoning
Fuzzy Multiple Attribute Decision Making: Methods and Applications
Fuzzy Multiple Attribute Decision Making: Methods and Applications
An Underlying Memory Model to Support Case Retrieval
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Possibility as similarity; the semantics of fuzzy logic
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Conceptual Distance of Numerically Specified Case Features
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
Design of a fuzzy expert system for determination of coronary heart disease risk
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
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The case-based reasoning (CBR) architecture described in this paperrepresents a substantive advancement in the representation ofcase-knowledge. It addresses three major problems found in currentCBR schemes: 1) Insufficient treatment of abstract case features‘context-dependent characteristics. 2) Lack of a methodical supportfor atomic and structured case features that contain and representimprecisely specified quantities. 3) And little account forclustering and organising cognate cases into conceptually overlapping categories.To overcome the representational inadequacyresulting from those deficiencies, this work proposes two modellingfundamentals, namely, fuzzy primitive and fuzzy complex abstractfeatures. These allow a flexible, polymorphic encoding of casecharacteristics as real numbers, linguistic terms, fuzzy numbers andfuzzy complex objects respectively. Based on this concept, it ispossible to systematically organise a case base in fuzzy categories,reflecting real-world case clusters. In the presented scheme, aprototype case and its associated approximation scales form the basisto realise a versatile mechanism to represent the context-specificidiosyncrasies of fuzzy abstract case features. Case categories,fuzzy abstract features, cases, and the approximation scale conceptare modelled as self-contained, operational entities. Theyco-operatively concert their services when they categorise anunclassified problem description (target case), and locate relevantstored cases. Applied to the Coronary Heart Disease risk assessmentdomain, the proposed architecture has proven to be highly adequatefor capturing and efficiently processing case-knowledge. Moreover, asthis scheme is designed upon well-established object-orientedprinciples, it has been shown that it can seamlessly integrate in awider, more general knowledge management regime.