Diagnostic Analytics on Agriculture with Fuzzy Classification

Author(s):  
R. Umarani ◽  
R. Suguna

Agriculture is the main domain and need of India. The country is second place in the world in agriculture. Cropping is the main part of agriculture. Various crops like millets, fruits, vegetables, oil seeds are produced and exported to other countries every year. So, various innovative technologies are used to improve the productivity of crops in agriculture. Rainfall is most important for growing crops. The water level for the crops based on rainfall has some uncertainty. Fuzzy regression analysis is one of the methods based on regression analysis that is used to handle fuzzy parameters and crisp data and vice versa. Linear fuzzy regression is one of the methods of fuzzy regression analysis to handle fuzzy parameters. This chapter explores fuzzy classification, which is based on fuzzy regression analysis, and it is compared with other classification algorithms on the agriculture data.

2011 ◽  
Vol 181 (19) ◽  
pp. 4154-4174 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Riccardo Massari ◽  
Adriana Santoro

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Pingping Gao ◽  
Yabin Gao

This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.


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