Applying Machine Learning Models to Identify Forest Cover

被引：0

作者：

Johnson, Peter ^{[1
]}

Abdelfattah, Eman ^{[2
]}

机构：

[1] Sacred Heart Univ, Sch Comp Sci & Engn, Fairfield, CT 06825 USA

[2] Ramapo Coll, Sch Theoret & Appl Sci, Mahwah, NJ USA

来源：

2018 9TH IEEE ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2018年

关键词：

Random Forest; Logistic Regression; Stochastic Gradient Descent; Support Vector Machine;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents an application of several classification techniques on forested lands data. More specifically, testing the efficiency of each classifier in its ability to identify a specific forest cover type. Four classifiers were used in the study, and testing was performed with both unscaled data and data scaled via two different methods. The study completes successfully with a stand-out algorithm that easily exceeded its peers in the given task; the Random Forest classifier. It concludes with speculation on how this algorithm, and the study itself, can be built-upon in the future.

引用

页码：471 / 474

页数：4

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