Practical Outcomes of Applying Ensemble Machine Learning Classifiers to High-Throughput Screening (HTS) Data Analysis and Screening

2008 ◽  
Vol 48 (11) ◽  
pp. 2196-2206 ◽  
Author(s):  
Kirk Simmons ◽  
John Kinney ◽  
Aaron Owens ◽  
Daniel A. Kleier ◽  
Karen Bloch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning

scholarly journals Prediction of plant lncRNA by ensemble machine learning classifiers

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Caitlin M. A. Simopoulos ◽  
Elizabeth A. Weretilnyk ◽  
G. Brian Golding
Keyword(s):  
Machine Learning ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning

A novel classifier for improving wetland mapping by integrating image fusion techniques and ensemble machine learning classifiers

2021 ◽  
pp. 101426
Author(s):  
Javed Mallick ◽  
Swapan Talukdar ◽  
Shahfahad ◽  
Swades Pal ◽  
Atiqur Rahman
Keyword(s):  
Machine Learning ◽  
Image Fusion ◽  
Wetland Mapping ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Ensemble Machine Learning

scholarly journals Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers

2021 ◽  
Vol 13 (8) ◽  
pp. 1494
Author(s):  
James M. Muthoka ◽  
Edward E. Salakpi ◽  
Edward Ouko ◽  
Zhuang-Fang Yi ◽  
Alexander S. Antonarakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Invasive Plant ◽  
Rangeland Management ◽  
Machine Learning Classifiers ◽  
Spectral Bands ◽  
Learning Classifiers ◽  
Ensemble Machine Learning ◽  
Study Results ◽  
Semi Arid ◽  
Sentinel 2

Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems.

Testing Machine Learning Classifiers based on Compositional Metamorphic Relations

2020 ◽  
Vol 16 (1) ◽  
pp. 67
Author(s):  
Minghua Jia ◽  
Xiaodong Wang ◽  
Yue Xu ◽  
Zhanqi Cui ◽  
Ruilin Xie
Keyword(s):  
Machine Learning ◽  
Testing Machine ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Botnet Detection with Machine Learning Classifiers

2020 ◽  
Vol 51 (2) ◽  
pp. 329-335
Author(s):  
POKURI ASHOK KUMAR
Keyword(s):  
Machine Learning ◽  
Botnet Detection ◽  
Machine Learning Classifiers ◽  
Learning Classifiers
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