Approach of hybrid soft computing for agricultural data classification

Shi Lei, Duan Qiguo, Si Haiping, Qiao Hongbo, Zhang Juanjuan, Ma Xinming

Abstract


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.
Keywords: agricultural data, soft computing, rough set, support vector machine, ensemble learning, classification
DOI: 10.3965/j.ijabe.20150806.1312

Citation: Shi L, Duan Q G, Si H P, Qiao H B, Zhang J J, Ma X M. Approach of hybrid soft computing for agricultural data classification. Int J Agric & Biol Eng, 2015; 8(6): 54－61.

Keywords


agricultural data, soft computing, rough set, support vector machine, ensemble learning, classification

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References


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