Soft computing is an important computational paradigm, and it provides the capability of flexible information\nprocessing to solve real world problems. Agricultural data classification is one of the important applications of computing\ntechnologies in agriculture, and it has become a hot topic because of the enormous growth of agricultural data available.\nSupport vector machine is a powerful soft computing technique and it realizes the idea of structural risk minimization principle\nto find a partition hyperplane that can satisfy the class requirement. Rough set theory is another famous soft computing\ntechnique to deal with vague and uncertain data. Ensemble learning is an effective method to learn multiple learners and\ncombine their decisions for achieving much higher prediction accuracy. In this study, the support vector machine, rough set\nand ensemble learning were incorporated to construct a hybrid soft computing approach to classify the agricultural data. An\nexperimental evaluation of different methods was conducted on public agricultural datasets. The experimental results\nindicated that the proposed algorithm improves the performance of classification effectively.
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