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Gradient boosting for the prediction of gas chromatographic retention indices

Dmitriy D. Matyushin, Anastasia Yu. Sholokhova, Aleksey K. Buryak
2019 Сорбционные и хроматографические процессы  
Various machine learning methods are used for this task, but methods based on decisiontrees, in particular gradient boosting, are not used widely.  ...  A neural network with one hidden layer (90 hidden nodes) is used for the comparison. The same data sets and the set of descriptors are used for the neural network and gradient boosting.  ...  Gradient boosting is extremely widely used and helpful machine learning method, but still there are no works on the use of gradient boosting for the RI prediction.  ... 
doi:10.17308/sorpchrom.2019.19/2223 fatcat:2t3r5rwecnddlalkaqv3fydgim

A Molecular Image-Based Novel Quantitative Structure-Activity Relationship Approach, Deepsnap-Deep Learning and Machine Learning

Yasunari Matsuzaka, Yoshihiro Uesawa
2020 Current Issues in Molecular Biology  
The quantitative structure-activity relationship (QSAR) approach has been used in numerous chemical compounds as in silico computational assessment for a long time.  ...  Particularly, the three- dimensional structure of chemical compounds has been gaining increasing attention owing to the representation of a large amount of information.  ...  To date, machine learning algorithms, such as random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (Xgboost), Light Gradient Boosting Machine (LightGBM), Category Boosting (CATBoost  ... 
doi:10.21775/cimb.042.455 pmid:33339777 fatcat:3psyj34jwrbdbk4r5ljij6piri


Anjali, Shivendra Dubey, Rakesh Shivhare, Mukesh Dixit
2018 Journal of Harmonized Research in Applied Science  
Gradient boosting is an intense machine learning method presented by Friedman [2] .  ...  The loss function L: Y × A → R+ gives a quantitative measure of the loss brought about from picking activity a when the genuine  ...  Tree Boosting Methods: Using trees as base models for boosting is a very popular choice.  ... 
doi:10.30876/johr.6.4.2018.276-285 fatcat:35no2fac2zcrfoy7unnajs7cii

Accurate Liver Disease Prediction with Extreme Gradient Boosting

2019 International Journal of Engineering and Advanced Technology  
Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets.  ...  Boosting techniques are often used in Machine learning to get maximum classification accuracy.  ...  [11] Compared eXtreme Gradient Boosting (XGBoost) with Single-Task Deep Neural Nets and Random Forest for Quantitative Structure-Activity Relationships on thirty in-house data sets.  ... 
doi:10.35940/ijeat.f8684.088619 fatcat:vsr4pe7pwzapbm2qyzlv6bo2jq

A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach

Ricardo Costa-Mendes, Tiago Oliveira, Mauro Castelli, Frederico Cruz-Jesus
2020 Education and Information Technologies : Official Journal of the IFIP technical committee on Education  
A multilinear regression model is used in parallel with random forest, support vector machine, artificial neural network and extreme gradient boosting machine stacking ensemble implementations.  ...  This article uses an anonymous 2014-15 school year dataset from the Directorate-General for Statistics of Education and Science (DGEEC) of the Portuguese Ministry of Education as a means to carry out a  ...  The extreme gradient boosting machine (Chen and Guestrin 2016) is a regularised version of the gradient boosting machine.  ... 
doi:10.1007/s10639-020-10316-y fatcat:uotpj6owtjb2rmoax34zjrlr6a

A Regularization-Based eXtreme Gradient Boosting Approach in Foodborne Disease Trend Forecasting

Shanen Chen, Jian Xu, Lili Chen, Xi Zhang, Li Zhang, Jinfeng Li
2019 Studies in Health Technology and Informatics  
This study develops a regularization-based eXtreme gradient boosting approach for foodborne disease trend forecasting considering environmental effects to capture dependencies hidden in foodborne disease  ...  A real case in Shanghai, China was studied to validate our proposed model along with comparisons to traditional and benchmark algorithms for foodborne disease prediction.  ...  As one of popular gradient boosting models, tree boosting is a highly effective and widely used machine learning method.  ... 
doi:10.3233/shti190360 pmid:31438060 fatcat:k5z75g6sdzc77jx324qhn67sge

Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction

Lanfa Liu, Min Ji, Yunyun Dong, Rongchung Zhang, Manfred Buchroithner
2016 Remote Sensing  
Gradient-boosting regression models built using XGBoost library with soil spectral library were developed to estimate N, pH and soil organic carbon (SOC) contents.  ...  Visible and near-infrared diffuse reflectance spectroscopy has been demonstrated to be a fast and cheap tool for estimating a large number of chemical and physical soil properties, and effective features  ...  All authors discussed the basic structure of the manuscript. L.L. wrote the draft, and M.B. reviewed and edited it. All authors read and approved the submitted manuscript.  ... 
doi:10.3390/rs8121035 fatcat:o7xnhzyz4refnhzfcfj2pmg5n4

Prediction and optimization of epoxy adhesive strength from a small dataset through active learning

Sirawit Pruksawan, Guillaume Lambard, Sadaki Samitsu, Keitaro Sodeyama, Masanobu Naito
2019 Science and Technology of Advanced Materials  
A Gradient boosting machine learning model was used for the successive prediction of the adhesive joint strength in the active learning pipeline, and the model achieved a respectable accuracy with a coefficient  ...  Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials.  ...  Susumu Takamori of NIMS for his instrumental support on the 50-kN universal tensile testing machine. Disclosure statement No potential conflict of interest was reported by the authors.  ... 
doi:10.1080/14686996.2019.1673670 pmid:31692965 pmcid:PMC6818118 fatcat:ye3ny6e5jfeirfyxxpagumcqe4

n Artificial Intelligence Approach Based on Hybrid CNN-XGB Model to Achieve High Prediction Accuracy through Feature Extraction, Classification and Regression for Enhancing Drug Discovery in Biomedicine

Mukesh Madanan, Biju T. Sayed, Nurul Akhmal Mohd Zulkefli, Nitha C. Velayudhan
2021 International Journal of Biology and Biomedical Engineering  
as the input data of the XGBoost for drug response prediction.  ...  Herein, the paper proposes a deep neural network structure as the Convolutional Neural Network (CNN) to detain the gene expression features of the cell line and then use the resulting abstract features  ...  Deep Neural Network (DNN) was utilized as a sensible Quantitative Structure-Activity Relationship (QSAR) technique [15] , and can easily outperform Random Forest in most experiments.  ... 
doi:10.46300/91011.2021.15.22 fatcat:iu6ujquoarba3aqhph32s5pina

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu
2020 Journal of Cheminformatics  
It also benefits and accelerates the researches in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR).  ...  In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting  ...  There is growing interest in applications of machine-learning techniques in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modeling research  ... 
doi:10.1186/s13321-020-0417-9 pmid:33430997 fatcat:7xbmhvnknreuze3guoytbabp7m

Soil pH and plant diversity shape soil bacterial community structure in the active layer across the latitudinal gradients in continuous permafrost region of Northeastern China

Baihui Ren, Yuanman Hu, Baodong Chen, Ying Zhang, Jan Thiele, Rongjiu Shi, Miao Liu, Rencang Bu
2018 Scientific Reports  
Here, a field investigation in the continuous permafrost region was conducted to collect 63 soil samples from 21 sites along a latitudinal gradient to assess the distribution pattern of microbial communities  ...  Both microbial richness and phylogenetic diversity decreased initially and then increased as the latitude increased.  ...  We are grateful to all the members of Mr Zhiwen Nie and Yanqing Huang, Miss Jinting Guo for their assistance during field sampling.  ... 
doi:10.1038/s41598-018-24040-8 pmid:29618759 pmcid:PMC5884794 fatcat:icaso24v3fedpniqtakpc4fi34

Predicting Blood–Brain Barrier Permeability of Marine-Derived Kinase Inhibitors Using Ensemble Classifiers Reveals Potential Hits for Neurodegenerative Disorders

Fabien Plisson, Andrew Piggott
2019 Marine Drugs  
We evaluated several regression and classification models, and found that our optimised classifiers (random forest, gradient boosting, and logistic regression) outperformed other models, with overall cross-validated  ...  Over the last 35 years, marine biodiscovery has yielded 471 natural products reported as kinase inhibitors, yet very few have been evaluated for BBB permeability.  ...  Capon and Nicholas Hamilton, both professors at the Institute for Molecular Bioscience, The University of Queensland for their early support and supervision.  ... 
doi:10.3390/md17020081 fatcat:lbpebeo6qzeevizc2ufwd4qt5y

Graph Based Link Prediction between Human Phenotypes and Genes [article]

Rushabh Patel, Yanhui Guo
2021 arXiv   pre-print
Results: The downstream link prediction task shows that the Gradient Boosting Decision Tree based model (LightGBM) achieved an optimal AUROC 0.904 and AUCPR 0.784.  ...  Methods: In this study, we developed a framework to predict links between human phenotype ontology (HPO) and genes.  ...  Gradient Boosting is a method which trains on the errors or residuals of previous models. This method is computationally efficient & fast as compared to other boosting methods.  ... 
arXiv:2105.11989v2 fatcat:sglfl4ox4nfnznez72ilkdwtg4

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Upma Singh, Mohammad Rizwan, Muhannad Alaraj, Ibrahim Alsaidan
2021 Energies  
Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power  ...  In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree,  ...  Data Availability Statement: The scrutiny of data and forecasting were accomplished with the openly available dataset that has been collected from the SCADA system for wind turbines in the northwestern  ... 
doi:10.3390/en14165196 fatcat:6xgb7bb5uvdpvabfzchctilzaq

Machine Learning-Based Energy System Model for Tissue Paper Machines

Huanhuan Zhang, Jigeng Li, Mengna Hong
2021 Processes  
The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy  ...  Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient  ...  Extreme Gradient Boosting Tree Model Extreme Gradient Boosting Tree Model Extreme Gradient Boosting Tree Model The gradient boosted regression tree (GBRT) is an ensemble machine learning method that  ... 
doi:10.3390/pr9040655 fatcat:dkncwtx7n5bbzco4ys5ttr6rhm
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