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An introduction to multivariate adaptive regression splines for the cane industry

机译：甘蔗行业的多元自适应回归样条简介

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摘要

Industries strive to find the balance between increased productivity and future sustainability of production. To this end, the sugar cane industry maintains records from each farm about CCS (commercial cane sugar content (%)), total cane yield, cane varieties and growing conditions throughout each region. A challenge that the cane industry faces is how to accurately extract useful information from this vast array of data to better understand and improve the production system. Data mining methods have been developed to search large data sets for hidden patterns. This paper introduces a powerful data mining method known as Multivariate Adaptive Regression Splines (MARS). By applying the MARS methodology to model CCS production data from the Herbert district, a model was produced for the 2005 harvest period. This model produced a north-south geographic separation between low and high CCS producing farms in line with recorded CCS values. The model was also able to identify farm groupings which contributed to lower, modelled CCS values, relative to other farms. A brief investigation on the isolated effects of variety was also conducted.
机译：工业界努力在提高生产率和未来生产可持续性之间找到平衡。为此，甘蔗业保留了每个农场有关CCS（商业甘蔗糖含量（％）），总甘蔗产量，甘蔗品种和整个地区生长条件的记录。甘蔗业面临的挑战是如何从海量数据中准确提取有用信息，以更好地理解和改善生产系统。已经开发了数据挖掘方法来搜索大型数据集以查找隐藏模式。本文介绍了一种强大的数据挖掘方法，称为多元自适应回归样条线（MARS）。通过将MARS方法应用于来自赫伯特地区的CCS生产数据模型，产生了2005年收获期的模型。根据记录的CCS值，此模型在低CCS和高CCS生产农场之间产生了南北地理隔离。该模型还能够识别相对于其他农场导致较低的CCS建模值的农场分组。还对品种的孤立效应进行了简要调查。

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  • 来源
    《International Sugar Journal》 |2011年第1350期|p.460|共1页
  • 作者

    Y.L. Everingham; J. Sexton;

  • 作者单位

    School of Engineering and Physical Sciences, James Cook University;

    School of Engineering and Physical Sciences, James Cook University;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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