The concept learning in the theory of rough sets

Author

Zhang, Qun-Feng ; Jiang, Yu-ting ; Li, Zhi-qiang

Author_Institution

Key Lab. of Machine Learning & Comput. Intell., Hebei Univ., Baoding

Volume

1

fYear

2008

fDate

12-15 July 2008

Firstpage

337

Lastpage

339

Abstract

Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.

Keywords

approximation theory; decision tables; learning (artificial intelligence); rough set theory; PAC-learnability; concept learning; decision table; knowledge reduction; rough sets theory; Computational intelligence; Computer industry; Computer science; Cybernetics; Educational institutions; Knowledge engineering; Machine learning; Machine learning algorithms; Mathematics; Rough sets; Concept Learning; PAC-Learnability; Rough Set; Sample Complexity;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620427

Filename

4620427