Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Inhibitory Rules in Data Analysis: A Rough Set Approach
Inhibitory Rules in Data Analysis: A Rough Set Approach
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Discovering rules-based similarity in microarray data
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
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This paper presents an ongoing research on the problem of assessing a similarity between objects in the context of classification. A new model of similarity is presented, called Rule-based Similarity (RBS), in which the similarity is expressed in terms of higher-level binary features of objects. Those features may be associated with decision rules derived from data and can be interpreted as arguments for a similarity or for a dissimilarity of the examined objects. The model was motivated by the feature contrast model of Amos Tversky. Its main aim is to simulate the human way of perceiving similar objects and at the same time to achieve a high accuracy in real life classification tasks. The partial results of conducted experiments confirm that the RBS is an interesting alternative to the commonly used distance-based similarity models.