Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection
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In this paper, rough, fuzzy and soft set approaches have been integrated to develop a multi-class classification model to assist the farmers in taking decision on crop cultivation for a given agriculture land. The model is divided into three major sections, namely weight calculation of variables, conversion of continuous data to fuzzified values and classification rule generation. Dominance-based rough set approach is used for the calculation of relative weights of variables. Fuzzy proximity relation is applied to convert the continuous data into fuzzified values. Bijective soft set approach is used to generate classification rules for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi. The developed model has been tested with agriculture dataset which showed 92% accuracy for the validation dataset and proved to be confident and robust for agriculture development. Further, the performance of the proposed model is compared with three popular classifiers such as naïve Bayes, support vector machine and J48. The obtained experimental results showed high predictive performance, and the potential of the proposed model is compared with the other classifiers.
KeywordsRough set Fuzzy proximity relation Soft set Grey relational analysis Agriculture crop Multi-class classification
This work forms part of the R and D activities of TIFAC-CORE in Automotive Infotronics located at VIT University, Vellore. The authors would like to thank DST, Government of India, for providing necessary hardware and software support for completing this work successfully.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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