Experimental Exploratory of Temporal Sampling Forest in Forest Fire Regression and Classification

Yee Jian Chew, S. Ooi, Y. Pang
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引用次数: 2

Abstract

Temporal Sampling Forest (TS-F) has been devoted to tackle the sequential data classification problem. It extends the robustness of random forest (RF) in handling the sequential data classification. However, it has not been used in the area of forest fire detection. Forest fire can be seen as a temporal phenomenon where it does not form in one day, but subsequently occurred due to the sequential changes of climates, human factors, and other affecting factors. Therefore, this paper is aim to tackle the data of forest fire from two perspectives, which are regression analysis and classification problem by using TS-F. The root mean square error (RMSE) obtained for regression analysis in the 1st dataset is 61.35 while the classification accuracy of the 2nd dataset is 84.18 %.
森林火灾回归分类中时间采样森林的实验探索
时序采样森林(Temporal Sampling Forest, TS-F)一直致力于解决序列数据分类问题。它扩展了随机森林在处理序列数据分类方面的鲁棒性。然而,它尚未在森林火灾探测领域得到应用。森林火灾可以看作是一种时间现象,它不是在一天内形成的,而是由于气候、人为因素和其他影响因素的连续变化而随后发生的。因此,本文拟从回归分析和分类问题两个方面对森林火灾数据进行处理。第一个数据集进行回归分析得到的均方根误差(RMSE)为61.35,第二个数据集的分类准确率为84.18%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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