Discernibility-based algorithm for discretizing continuous variables of Credal network

Author

Qu, Ying ; Li, Qing-Heng ; Jia, Jian

Author_Institution

Sch. of Econ. & Manage., Hebei Univ. of Sci. & Technol., Shijiazhuang, China

Volume

5

fYear

2009

fDate

12-15 July 2009

Firstpage

2522

Lastpage

2526

Abstract

Having analyzed that the discretization algorithm of rough set and Boolean reasoning approach didn´t work well in Bayesian networks, a new algorithm for discretizing continuous variables is put forward to distinguish two samples by the value of candidate cuts while not by the intervals determined by two candidate cuts. The application case indicates that the improved algorithm can reduce preferably the space complexity and time complexity of the discretization. It is effective on discretizing continuous variables of Credal network.

Keywords

computational complexity; inference mechanisms; rough set theory; Boolean reasoning approach; Credal network; discernibility-based algorithm; discretizing continuous variables; rough set theory; space complexity; time complexity; Algorithm design and analysis; Bayesian methods; Conference management; Cybernetics; Electronic mail; Gaussian distribution; Machine learning; Machine learning algorithms; Set theory; Technology management; Candidate cuts; Credal network; Rough Set theory; Variable discretization;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212635

Filename

5212635