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

Nguyen, Tuan Trung; Skowron, Andrzej

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].

MSC:

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

Keywords:

Rough mereology; concept approximation; domain knowledge approximation; machine learning; handwritten digit recognition