Classification Learning System Based on Multi-objective GA and Microthermal Weather Forecast

Author

Zhang Hongwei ; Xu Jingxun ; Zou Shurong

Author_Institution

Coll. of Comput., Chengdu Univ. of Inf. Technol., Chengdu, China

fYear

2011

fDate

5-7 Aug. 2011

Firstpage

301

Lastpage

304

Abstract

A new classification learning system based on multi-objective GA is proposed in this paper. Firstly, the continuous attributes of samples are made discretion with a supervised segmentation method, so generaLization and intelLigibiLity of machine learning are improved. Moreover, comparison and selection mechanism based on partial order in set theory are infused into multi-objective GA. They enhance the abiLity to choose better chromosomes. The new algorithm is used to forecast microthermal weather in northern ZheJiang province. The experiment result indicates that it has unique intelLigence, higher accuracy.

Keywords

generalisation (artificial intelligence); genetic algorithms; geophysics computing; learning (artificial intelligence); pattern classification; set theory; weather forecasting; China; classification learning system; generalization improvement; intelligibility improvement; machine learning; microthermal weather forecast; multiobjective GA; northern ZheJiang province; partial order; selection mechanism; set theory; supervised segmentation method; Biological cells; Classification algorithms; Encoding; Learning systems; Neodymium; Weather forecasting; Machine Learning; Microthermal Weather Forecast; Multi-objective GA; Supervised Segmentation Method;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on

Conference_Location

Zhangjiajie, Hunan

Print_ISBN

978-1-4577-0755-1

Electronic_ISBN

978-0-7695-4455-7

Type

conf

DOI

10.1109/ICDMA.2011.80

Filename

6052011