基于多元统计方法的农业发展多类模型
Multiclass Model for Agriculture development using Multivariate Statistical method
摘要：
马氏田口系统（MTS）是一种多变量统计方法，广泛用于特征选择和二分类问题。MTS中正交表和信噪比的计算使得当分类问题涉及的因素较多时，算法变得复杂。此外，该决定是基于数据集的正常和异常观测的准确性。本文提出了一种基于正态观测和马氏距离的改进马氏田口系统（IMTS）农业发展多类模型。与作物栽培相关的26个输入因子已被确定，并聚类为6个主要因素的模型的发展。多类模型的开发与考虑因素的相对重要性。定义了一个目标函数，用于水稻、甘蔗和花生三种作物的分类。分类结果进行了验证，从农业专家在该领域工作的结果。与其他传统的分类器模型相比，该分类器提供了100%的准确率、召回率、精确率和0%的出错率。
英文摘要：
Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.
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