The Strength of Weak Learnability
Machine Learning
Strategies for combining classifiers employing shared and distinct pattern representations
Pattern Recognition Letters - special issue on pattern recognition in practice V
Validation of voting committees
Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Principles of data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
FERNN: An Algorithm for Fast Extraction of Rules fromNeural Networks
Applied Intelligence
Feedforward Neural Network Construction Using Cross Validation
Neural Computation
Hierarchical classifier with overlapping class groups
Expert Systems with Applications: An International Journal
PPAM'05 Proceedings of the 6th international conference on Parallel Processing and Applied Mathematics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Risk estimation for hierarchical classifier
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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A system of rule extraction out of a complex hierarchical classifier is proposed in this paper. There are several methods for rule extraction out of trained artificial neural networks (ANN's), but these methods do not scale well, i.e.results are satisfactory for small problems. For complicated problems hundreds of rules are produced, which are hard to govern.In this paper a hierarchical classifier with a tree-like structure and simple ANN's at nodes, is presented, which splits the original problem into several sub-problems that overlap. Node classifiers are all weak(i.e.with accuracy only better than random), and errors are corrected at lower levels. Single sub-problems constitute of examples that were hard to separate. Such architecture is able to classify better than single network models.At the same time if---thenrules are extracted, which only answer which sub-problem a given example belongs to. Such rules, by introducing hierarchy, are simpler and easier to modify by hand, giving also a better insight into the original classifier behaviour.