Soft computing is an important computational paradigm,and it provides the capability of flexible information processing to solve real world problems.Agricultural data classification is one of the important application...Soft computing is an important computational paradigm,and it provides the capability of flexible information processing to solve real world problems.Agricultural 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 available.Support 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 requirement.Rough set theory is another famous soft computing technique to deal with vague and uncertain data.Ensemble learning is an effective method to learn multiple learners and combine their decisions for achieving much higher prediction accuracy.In this study,the support vector machine,rough set and ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural data.An experimental evaluation of different methods was conducted on public agricultural datasets.The experimental results indicated that the proposed algorithm improves the performance of classification effectively.展开更多
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)+1 种基金the Key Science and Technology Project of Henan Province(Grant No.142102210054)Henan Province Key Project of Science and Technology(Grant No.131100110400)。
文摘Soft computing is an important computational paradigm,and it provides the capability of flexible information processing to solve real world problems.Agricultural 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 available.Support 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 requirement.Rough set theory is another famous soft computing technique to deal with vague and uncertain data.Ensemble learning is an effective method to learn multiple learners and combine their decisions for achieving much higher prediction accuracy.In this study,the support vector machine,rough set and ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural data.An experimental evaluation of different methods was conducted on public agricultural datasets.The experimental results indicated that the proposed algorithm improves the performance of classification effectively.
