Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Ordinal Credibility Coefficient --- A New Approach in the Data Credibility Analysis
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Credibility coefficients in ARES rough set exploration system
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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ARES System was a data analysis tool supporting Rough Set theory. It has been expanded to cover other approaches like Emerging Patterns and Support Vector Machine. A special feature of ARES System is ability to identify exceptional objects within information systems by using credibility coefficients. The credibility coefficient is a measure, which attempts to weigh up a degree of typicality of each object in respect to the rest of information system. The paper presents an idea of credibility coefficients based on SVM approach. The new coefficients are compared with the others ones available in the ARES System.