Approach of hybrid soft computing for agricultural data classification

作     者：Shi Lei Duan Qiguo Si Haiping Qiao Hongbo Zhang Juanjuan Ma Xinming 

作者机构：College of Information and Management ScienceHenan Agricultural UniversityZhengzhou 450002China Collaborative Innovation Center of Henan Grain CropsZhengzhou 450002China Zhengzhou Commodity ExchangeZhengzhou 450008China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2015年第8卷第6期

页      面：54-61页

核心收录：

学科分类：0710[理学-生物学] 0810[工学-信息与通信工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the National Natural Science Foundation of China(Grant No.31501225) the National“Twelfth Five-Year”Plan for Science&Technology Support Program(Grant No.2014BAD10B06) the Key Science and Technology Project of Henan Province(Grant No.142102210054) Henan Province Key Project of Science and Technology(Grant No.131100110400) 

主　　题：agricultural data soft computing rough set support vector machine ensemble learning classification 

摘      要：Soft computing is an important computational paradigm,and it provides the capability of flexible information processing to solve real world *** data classification is one of the important applications of computing technologies in agriculture,and it has become a hot topic because of the enormous growth of agricultural data *** vector machine is a powerful soft computing technique and it realizes the idea of structural risk minimization principle to find a partition hyperplane that can satisfy the class *** set theory is another famous soft computing technique to deal with vague and uncertain *** learning is an effective method to learn multiple learners and combine their decisions for achieving much higher prediction *** this study,the support vector machine,rough set and ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural *** experimental evaluation of different methods was conducted on public agricultural *** experimental results indicated that the proposed algorithm improves the performance of classification effectively.