“Change-glasses” approach in pattern recognition
Pattern Recognition Letters
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Methods for combining experts' probability assessments
Neural Computation
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal linear combinations of neural networks
Neural Networks
Lazy learning
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Machine Learning
Comparative Performance of Rule Quality Measures in an InductionSystem
Applied Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Induction of Rules Subject to a Quality Constraint: Probabilistic Inductive Learning
IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Branch and Bound Algorithm for Computing k-Nearest Neighbors
IEEE Transactions on Computers
Adaptive mixtures of local experts
Neural Computation
Combined approach to pattern classification in parametric case
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
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
Combining rule-based and sample-based classifiers – probabilistic approach
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Classifier ensemble for an effective cytological image analysis
Pattern Recognition Letters
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
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This work will present a review of the concept of classifier combination based on the combined discriminant function. We will present a Bayesian approach, in which the discriminant function assumes the role of the posterior probability. We will propose a probabilistic interpretation of expert rules and conditions of knowledge consistency for expert rules and learning sets. We will suggest how to measure the quality of learning materials and we will use the measure mentioned above for an algorithm that eliminates contradictions in the rule set. In this work several recognition algorithms will be described, based on either: (i) pure rules, or; (ii) rules together with learning sets. Furthermore, the original concept of information unification, which enables the formation of rules on the basis of learning set or learning set on the basis of rules will be proposed. The obtained conclusions will serve as a spring-board for the formulation of new project guidelines for this type of decision-making system. At the end, experimental results of the proposed algorithms will be presented, both from computer generated data and for a real problem from the medical diagnostics field. © 2012 Wiley Periodicals, Inc.