A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Prototype-based threshold rules
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A hybrid system with regression trees in steel-making process
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Computational complexity reduction and interpretability improvement of distance-based decision trees
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Evolutionary optimized forest of regression trees: application in metallurgy
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Extraction of prototype-based threshold rules using neural training procedure
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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In many cases it is better to extract a set of decision trees and a set of possible logical data descriptions instead of a single model. The trees that include premises with constraints on the distances from some reference points are more flexible because they provide nonlinear decision borders. Methods for creating heterogeneous forests of decision trees based on Separability of Split Value (SSV) criterion are presented. The results confirm their usefulness in understanding data structures.