Noise modelling and evaluating learning from examples
Artificial Intelligence
Machine Learning
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Editorial: Hybrid learning machines
Neurocomputing
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
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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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The predictive accuracy of classifiers is determined among others by the quality of data. This important property of data is strongly affected by such factors as the number of erroneous or missing attributes present in the dataset. In this paper we show how those factors can be handled by introducing the levels of abstraction in data definition. Our approach is especially valuable in cases where giving the precise value of an attribute is impossible for a number of reasons as for example lack of time or knowledge. Furthermore, we show that increasing the level of precision for an attribute significantly increase predictive accuracy, especially when it is done for the attribute with high information gain.