Title :
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
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;
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
DOI :
10.1109/ICDMA.2011.80
