Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest

Matin, S.S.; Farahzadi, Leila; Makaremi, S.; Chelgani, S. Chehreh; Sattari, Gh.

doi:10.1016/j.asoc.2017.06.030

Cited by 140 publications

(51 citation statements)

References 55 publications

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“…Initially, the algorithm produces bootstrapped data and assigns a decision tree for each data subset. These decision trees (i.e., single classifiers) lay the ground for the final prediction by means of forming forest (i.e., group of classifiers) through bootstrap aggregation (bagging) samples, which means fitting trees to subsampled data and averaging all the trees .…”

Section: Methodsmentioning

confidence: 99%

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

et al. 2019

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Nowadays, modern nanomaterial research is complemented by machine learning methods to reduce experimental costs and process time. With this motivation, here, we implemented artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) methods to predict the mechanical properties of threecomponent nanocomposite films consisting of polyvinyl alcohol (PVA) crosslinked 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized cellulose nanofibers (TOCNFs) and either ammonium zirconium carbonate (AZC) or glyoxal (Gx) using the mechanical properties of mono-component TOCNF films and two-component nanocomposites containing PVA, AZC, or Gx-crosslinked TOCNF as the input of prediction system. Prediction methods were evaluated with performance indicators and experimental data. Overall, MLR performed with least accuracy, whereas ANN prediction displayed the lowest error followed closely by RF. Additionally, the physically or/and chemically crosslinked hybrid films with optimized amount of crosslinkers resulted in structures with a strength to rupture that was significantly higher than that of the pure nanocellulose films (increases of up to~90% in tensile strength and~70% in Young's modulus). POLYM. COM-POS., 40:4013-4022, 2019.

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Section: Methodsmentioning

confidence: 99%

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

et al. 2019

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“…Inspired from an election, each decision tree acts as a voter. The set of all votes for the final decision is used to improve the predictions' accuracy [37][38][39]. The background of the RF algorithm can be described according to the following pseudo-code ( Figure 1).…”

Section: Random Forestmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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“…It assists to optimize the number of variables which typically have to be measured during a process, reduce the number of parameters, and to save cost and time. In other words, collinearity may lead to measuring various parameters that show the same concept [47][48][49]. Therefore, FS was used through the value of measured parameters (pH, ORP, Fe T , and DO) in the control tests to find the most effective parameters on microorganisms count (MC).…”

Section: Feature Selectionmentioning

confidence: 99%

Effects of Conventional Flotation Frothers on the Population of Mesophilic Microorganisms in Different Cultures

et al. 2019

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Bioleaching is an environment-friendly and low-investment process for the extraction of metals from flotation concentrate. Surfactants such as collectors and frothers are widely used in the flotation process. These chemical reagents may have inhibitory effects on the activity of microorganisms through a bioleaching process; however, there is no report indicating influences of reagents on the activity of microorganisms in the mixed culture which is mostly used in the industry. In this investigation, influences of typical flotation frothers (methyl isobutyl carbinol and pine oil) in different concentrations (0.01, 0.10, and 1.00 g/L) were examined on activates of bacteria in the mesophilic mixed culture (Acidithiobacillus ferrooxidans, Leptospirillum ferrooxidans, and Acidithiobacillus thiooxidans). For comparison purposes, experiments were repeated by pure cultures of Acidithiobacillus ferrooxidans and Leptospirillum ferrooxidans in the same conditions. Results indicated that increasing the dosage of frothers has a negative correlation with bacteria activities while the mixed culture showed a lower sensitivity to the toxicity of these frothers in comparison with examined pure cultures. Outcomes showed the toxicity of Pine oil is lower than methyl isobutyl carbinol (MIBC). These results can be used for designing flotation separation procedures and to produce cleaner products for bio extraction of metals.

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Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest

Cited by 140 publications

References 55 publications

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Effects of Conventional Flotation Frothers on the Population of Mesophilic Microorganisms in Different Cultures

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