Rough set approach to incomplete information systems
Information Sciences: an International Journal
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
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The valued tolerance relation in incomplete information systems is an important extension model of the classical rough set theory. However, the general calculation method of tolerance degree needs to know the probability distribution of an information system in advance, and it is also difficult to select a suitable threshold. In this paper, a data-driven valued tolerance relation is proposed based on the idea of data-driven data mining. The new calculation method of tolerance degree and the auto-selection method of threshold do not require any prior domain knowledge except the data set. Experiment results show that the data-driven valued tolerance relation can get better and more stable classification results than the other extension models of the classical rough set theory.