Fuzzy sets in pattern recognition: methodology and methods
Pattern Recognition
Fuzzy relation equations theory as a basis of fuzzy modelling: an overview
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Genetic algorithms for learning in fuzzy relational structures
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A course in fuzzy systems and control
A course in fuzzy systems and control
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Application of rough sets theory to the sequential diagnosis
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Sequential classification via fuzzy relations
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Leukemia prediction from gene expression data—a rough set approach
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Fuzzy relational classifier trained by fuzzy clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern classification using fuzzy relational calculus
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Sequential classification task is typical in medical diagnosis, when the investigations of the patient's state are repeated several times. Such situation always takes place in the controlling of the drug therapy efficacy. A specific feature of this diagnosis task is the dependence between patient's states at particular instants, which should be taken into account in sequential diagnosis algorithms. In this paper methods for performing sequential diagnosis using fuzzy sets and rough sets theory are developed and evaluated. For both soft methodologies several algorithms are proposed which differ in kind of input data and in details of classification procedures for particular instants of decision process. Proposed algorithms were practically applied to the computer-aided medical problem of recognition of patient's acid-base equilibrium states. Results of comparative experimental analysis of investigated algorithms in respect of classification accuracy are also presented and discussed.