Rough set approach to domain knowledge approximation. (English) Zbl 1026.68644

Wang, Guoyin (ed.) et al., Rough sets, fuzzy sets, data mining, and granular computing. 9th international conference, RSFDGrC 2003, Chongqing, China, May 26-29, 2003. Proceedings. Berlin: Springer. Lect. Notes Comput. Sci. 2639, 221-228 (2003).
Summary: Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show how human expert’s domain knowledge expressed in natural language can be approximately translated by a machine learning recognition system. We present in details how the method performs on a system recognizing handwritten digits from a large digit database. Our approach is an extension of ideas developed in the rough mereology theory.
For the entire collection see [Zbl 1019.00015].


68T37 Reasoning under uncertainty in the context of artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
68T10 Pattern recognition, speech recognition
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