Handbook of logic in artificial intelligence and logic programming (vol. 3)
Methods for combining experts' probability assessments
Neural Computation
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Rule-based Classification Procedures Related to the Unprecisely Formulated Expert Rules
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Reasoning about Uncertainty
Consistency conditions of the expert rule set in the probabilistic pattern recognition
CIS'04 Proceedings of the First international conference on Computational and Information Science
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The present paper is devoted to the pattern recognition methods for combining heterogeneous sets of learning data: set of training examples and the set of expert rules with unprecisely formulated weights understood as conditional probabilities. Adopting the probabilistic model two concepts of recognition learning are proposed. In the first approach two classifiers trained on homogeneous data set are generated and next their decisions are combined using local weighted voting combination rule. In the second method however, one set of data is transformed into the second one and next only one classifier trained on homogeneous set of data is used. Presented algorithms were practically applied to the computer-aided diagnosis of acute renal failure in children and results of their classification accuracy are given.