The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICS AND COMPUTERS IN SIMULATION
Transactions ID Number: 19-388
Full Name: Hyontai Sug
Position: Associate Professor
Age: ON
Sex: Male
Address: Division of Computer and Information Engineering, Dongseo University, Busan, 617-716
Country: KOREA
Tel: 82-51-320-1733
Tel prefix: 82-51
Fax: 82-51-327-8955
E-mail address: hyontai@yahoo.com
Other E-mails: shtdaum@hanmail.net
Title of the Paper: a comparison of RBF networks and random forest in forecasting ozone day
Authors as they appear in the Paper: Hyontai Sug
Email addresses of all the authors: hyontai@yahoo.com
Number of paper pages: 8
Abstract: It is known that random forest has good performance for data sets containing some irrelevant features, and it is also known that the performance of random forest is very good at ozone day prediction data set that is supposed to have some irrelevant features. On the other hand, it is known that when data sets do not contain irrelevant features, RBF networks are good at prediction tasks. Moreover, in general, we do not have exact knowledge about irrelevant features, because data space is usually far greater than available data for training. So we want to test that the two facts are true or not for the ozone data set. Experiments were done with random forests and RBF networks using k-means clustering, and showed that RBF networks are slightly better than random forest for the ozone day prediction.
Keywords: RBF networks, random forest, decision trees, irrelevant features
EXTENSION of the file: .pdf
Special (Invited) Session: Ozone Day Prediction with Radial Basis Function Networks
Organizer of the Session: 646-724
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