scholarly journals Estimation of Tree Cover in an Agricultural Parkland of Senegal Using Rule-Based Regression Tree Modeling

2013 ◽  
Vol 5 (10) ◽  
pp. 4900-4918 ◽  
Author(s):  
Stefanie Herrmann ◽  
Andrew Wickhorst ◽  
Stuart Marsh
Keyword(s):  
Regression Tree ◽  
Tree Cover ◽  
Rule Based ◽  
Tree Modeling ◽  
Regression Tree Modeling

REGRESSION-TREE MODELING OF DESERT TORTOISE HABITAT IN THE CENTRAL MOJAVE DESERT

2000 ◽  
Vol 10 (3) ◽  
pp. 890-900 ◽  
Author(s):  
Mark C. Andersen ◽  
Joseph M. Watts ◽  
Jerome E. Freilich ◽  
Stephen R. Yool ◽  
Gery I. Wakefield ◽  
...  
Keyword(s):  
Mojave Desert ◽  
Regression Tree ◽  
Desert Tortoise ◽  
Tree Modeling ◽  
Regression Tree Modeling

scholarly journals Regression Tree Modeling of Spatial Pattern and Process Interactions

2017 ◽  
pp. 187-212 ◽  
Author(s):  
Trisalyn A. Nelson ◽  
Wiebe Nijland ◽  
Mathieu L. Bourbonnais ◽  
Michael A. Wulder
Keyword(s):  
Spatial Pattern ◽  
Regression Tree ◽  
Pattern And Process ◽  
Tree Modeling ◽  
Regression Tree Modeling

scholarly journals Köppen versus the computer: an objective comparison between the Köppen-Geiger climate classification and a multivariate regression tree

2011 ◽  
Vol 8 (2) ◽  
pp. 2345-2372 ◽  
Author(s):  
A. J. Cannon
Keyword(s):  
Multivariate Regression ◽  
Global Climate ◽  
Regression Tree ◽  
Climate Classification ◽  
Rule Based ◽  
Traditional System ◽  
Multivariate Regression Tree ◽  
Modern Computer ◽  
Computer Based ◽  
Algorithm Implementation

Abstract. A global climate classification is defined using a multivariate regression tree (MRT). The MRT algorithm is automated, which removes the need for a practitioner to manually define the classes; it is hierarchical, which allows a series of nested classes to be defined; and it is rule-based, which allows climate classes to be unambiguously defined and easily interpreted. Climate variables used in the MRT are restricted to those from the Köppen-Geiger climate classification. The result is a hierarchical, rule-based climate classification that can be directly compared against the traditional system. An objective comparison between the two climate classifications at their 5, 13, and 30 class hierarchical levels indicates that both perform well in terms of identifying regions of homogeneous temperature variability, although the MRT still generally outperforms the Köppen-Geiger system. In terms of precipitation discrimination, the Köppen-Geiger classification performs poorly relative to the MRT. The data and algorithm implementation used in this study are freely available. Thus, the MRT climate classification offers instructors and students in the geosciences a simple instrument for exploring modern, computer-based climatological methods.

scholarly journals Anomaly Detection using Machine Learning: A Rule Based Classification and Ordered Regression Tree with Real Time Datasets

2020 ◽  
Vol 9 (7) ◽  
pp. 108-114
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Regression Tree ◽  
Abnormal Behavior ◽  
K Nearest Neighbor ◽  
Rule Based ◽  
Original Dataset ◽  
System Data ◽  
Land Transactions

What we use the protection of system data and user credentials is still very dispensable presently in factual applications frequently used by common people. Also losing their assets and confidence level due to lack of knowledge about usage of applications and failure to grab the abnormal behavior. How system data and user credentials are helpful to creating clone by others causes of showing anomalous behavior and don’t know to protect from the anomalies and how it is avoid. In this paper we are presenting short-lived discussion on anomaly detection and its nature of impact showing on original true datasets related to daily land transactions, medical and social networking. This paper shows the significant usage of machine learning approach applied in anomaly detection to know the fact anomalies in various datasets took from different sources. Here we are using an updated CART called Rule based Classification and Ordered Regression Tree (RBT-ORT). This method is new one with combination of Decision Tree; Rules of Random Tree giving a new adorned rule sets in classification and regression to ensure the improvement in results compare to other techniques. Our work carried out on three datasets, two are taken from UCI repository for machine learning and other one is real and original dataset Land sale data pertaining to land transactions noted in the year 2016-18. Finally the results of anomaly detection using Classification and Ordered Regression Tree compare with other machine learning methods such as ID3, C4.5, C-RT, PLSDA, CHAID, C4.5 Rule, I (Improved) - C4.5, K-Nearest neighbor and Neural Networks.

scholarly journals Sub-pixel estimation of tree cover and bare surface densities using regression tree analysis

CERNE ◽  
2011 ◽  
Vol 17 (3) ◽  
pp. 411-416
Author(s):  
Carlos Augusto Zangrando Toneli ◽  
Luis Marcelo Tavares de Carvalho
Keyword(s):  
Numerical Models ◽  
Regression Tree ◽  
Tree Cover ◽  
Entire Study ◽  
The North ◽  
Regression Tree Analysis ◽  
Representing Tree ◽  
Tree Models ◽  
Continuous Fields ◽  
Bare Surface

Sub-pixel analysis is capable of generating continuous fields, which represent the spatial variability of certain thematic classes. The aim of this work was to develop numerical models to represent the variability of tree cover and bare surfaces within the study area. This research was conducted in the riparian buffer within a watershed of the São Francisco River in the North of Minas Gerais, Brazil. IKONOS and Landsat TM imagery were used with the GUIDE algorithm to construct the models. The results were two index images derived with regression trees for the entire study area, one representing tree cover and the other representing bare surface. The use of non-parametric and non-linear regression tree models presented satisfactory results to characterize wetland, deciduous and savanna patterns of forest formation.

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