High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework

2021 ◽  
pp. 126470
Author(s):  
Jiansen Zhao ◽  
Jinquan Lu ◽  
Xinqiang Chen ◽  
Zhongwei Yan ◽  
Ying Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Fidelity ◽  
Trajectory Prediction ◽  
Learning Framework ◽  
Ensemble Machine Learning

Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan

Landslides ◽  
2019 ◽  
Vol 17 (3) ◽  
pp. 641-658 ◽  
Author(s):  
Jie Dou ◽  
Ali P. Yunus ◽  
Dieu Tien Bui ◽  
Abdelaziz Merghadi ◽  
Mehebub Sahana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Framework ◽  
Ensemble Machine Learning ◽  
Mountainous Watershed

scholarly journals Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework

2020 ◽  
Vol 13 (10) ◽  
pp. 305
Author(s):  
Eugene Lin ◽  
Po-Hsiu Kuo ◽  
Yu-Li Liu ◽  
Younger W.-Y. Yu ◽  
Albert C. Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Treatment Response ◽  
Antidepressant Treatment ◽  
Learning Framework ◽  
Antidepressant Treatments ◽  
Ensemble Machine Learning ◽  
Predictive Algorithms ◽  
The Status

In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments.

scholarly journals NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Irantzu Anzar ◽  
Angelina Sverchkova ◽  
Richard Stratford ◽  
Trevor Clancy
Keyword(s):  
Machine Learning ◽  
Somatic Mutations ◽  
Learning Framework ◽  
Ensemble Machine Learning

A New Ensemble Machine-Learning Framework for Searching Sweet Spots in Shale Reservoirs

SPE Journal ◽  
2020 ◽  
Author(s):  
Jizhou Tang ◽  
Bo Fan ◽  
Lizhi Xiao ◽  
Shouceng Tian ◽  
Fengshou Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Framework ◽  
Ensemble Machine Learning ◽  
Shale Reservoirs ◽  
Sweet Spots
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