Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks

Geomorphology ◽  
2020 ◽  
Vol 371 ◽  
pp. 107331
Author(s):  
Prashant Baral ◽  
M. Anul Haq
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Random Forests ◽  
Spatial Prediction ◽  
Support Vector ◽  
Vector Machines

scholarly journals Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

2017 ◽  
Author(s):  
Eelke B. Lenselink ◽  
Niels ten Dijke ◽  
Brandon Bongers ◽  
George Papadatos ◽  
Herman W.T. van Vlijmen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Support Vector Machines ◽  
Random Forests ◽  
Deep Neural Networks ◽  
Direct Result ◽  
Support Vector ◽  
Temporal Validation ◽  
Vector Machines ◽  
The Mean

AbstractThe increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics.In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naive Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution.Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method (‘DNN_PCM’) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized DNN_PCM).Here, a standardized set to test and evaluate different machine learning algorithms in the context of multitask learning is offered by providing the data and the protocols.

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