Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Indiscernibility Relation for Continuous Attributes: Application in Image Recognition
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
On combined classifiers, rule induction and rough sets
Transactions on rough sets VI
Dominance-based rough set approach as a proper way of handling graduality in rough set theory
Transactions on rough sets VII
Rough feature selection for intelligent classifiers
Transactions on rough sets VII
On performance of DRSA-ANN classifier
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Application of DRSA-ANN classifier in computational stylistics
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Decision rule length as a basis for evaluation of attribute relevance
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Selection of characteristic features for a classification task is always crucial to high recognition ratio, regardlessly of the particular processing technique applied Most methodologies offer some inherent mechanisms of dimension reduction that lead to expression of available data in more succinct way, however, combining elements of distinctively different approaches to data analysis brings interesting conclusions as to the role of particular features and their influence on the power of the resulting classifier The paper presents research on such fusion of processing techniques, namely employing rough set based analysis of features for ANN classifier within stylometric studies on writing styles.