Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Rough set-based analysis of characteristic features for ANN classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Designing fusers on the basis of discriminants – evolutionary and neural methods of training
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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Rule-based and connectionist classifiers are typically named as two different approaches to recognition tasks. The first relies on induction of a set of rules that list conditions to be met for a decision to be applicable, while the latter means distribution of data and processing. Both solutions give satisfactory results in many classification problems yet their fusion and analysis of performance of the resulting hybrid classifier bring additional observations as to the role of particular features in the recognition. These observations are not based on domain knowledge, but on techniques employed and their inherent properties. The paper presents a study on performance of DRSA-ANN classifier applied within the domain of stylometry, a quantitative analysis of writing styles.