Ecological Footprint Model Using the Support Vector Machine Technique

Ma, Hui; Chang, Wenjuan; Cui, Guangbai

doi:10.1371/journal.pone.0030396

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Support Vector Machine (SVM) is a supervised machine-learning algorithm on the basis of statistical learning theory [ 53 , 60 – 65 ]. Due to the robustness, rapidness, and repeatability, machine-learning method is regarded as one of the best ways to efficiently classify numerous protein molecules.…”

Section: Discussionmentioning

confidence: 99%

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Liao

Zou

2016

Scientifica

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G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.

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Section: Discussionmentioning

confidence: 99%

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Liao

Zou

2016

Scientifica

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“…Using ML models, in particular, K-nearest neighbor regression, random forest regression and artificial neural networks to predict EF based on energy parameters (natural gas resources, coal resources, oil resources, wind resources, solar photovoltaic sources), hydropower, nuclear and other renewable resources) led to impressive results (Janković et al 2020). Ma et al (2012) developed an EF model to calculate the per capita EF of 24 countries. Their results show that the EF model based on support vector machine performs well.…”

Section: Figure 12mentioning

confidence: 99%

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Roumiani

Mofidi

2021

Preprint

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Paying attention to human activities in terms of land grazing infrastructure, crops, forest products and carbon impact, the so-called ecological impact (EF) is one of the most important economic issues in the world. In the present study, data from global databases were used. The ability of the penalized regression approach (PR including Ridge, Lasso and Elastic Net) and artificial neural network (ANN) to predict EF indices in the G-20 over the past two decades (1999–2018) was depicted and compared. For this purpose, 10-fold cross-validation was used to assess predictive performance and to specify a penalty parameter for PR models. Based on the results, a slight improvement in prediction performance was observed over linear regression. Using the Elastic Net model, more global macro indices were selected than Lasso. Although Lasso included only some indicators, it still had better predictive performance among PR models. Although the findings using PR methods were only slightly better than linear regression, their interest in selecting a subset of controllable indicators by shrinking the coefficients and creating a parsimonious model was apparent. As a result, penalized regression methods would be preferred, using feature selectivity and interpretive considerations rather than predictive performance alone. On the other hand, neural network-based models with higher values of coefficients of determination (R2) and values lower of RMSE than PR and OLS had significant performance and showed that they are more accurate in predicting EF. The results showed that the ANN network could provide considerable and appropriate predictions for EF indicators in the G-20 countries. predictions

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“…Machine learning is becoming increasingly popular in social and behavioral sciences, and is frequently used by researchers in different scientific fields to solve practical problems with complex data (e.g., Leach, O'Connor, Simpson, Rifai, & Mama, 2016;Ma, Chang, & Cui, 2012;Steele, Denaxas, Shah, Hemingway, & Luscombe, 2018a). Specifically, psychologists have begun to utilize these algorithms to analyze underlying factors in psychological phenomenon (e.g., Sauer et al, 2018), guide improvements of current treatments, and use previous patients records to make data-driven decisions for incoming patients (e.g., Zilcha-Mano, Errázuriz, Yaffe-Herbst, German, & DeRubeis, 2019).…”

Section: Introductionmentioning

confidence: 99%

Lasso and Group Lasso with Categorical Predictors: Impact of Coding Strategy on Variable Selection and Prediction

Huang¹,

Ak²

2020

Preprint

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Machine learning methods are being increasingly adopted in psychological research. Lasso performs variable selection and regularization, and is particularly appealing to psychology researchers because of its connection to linear regression. Researchers conflate properties of linear regression with properties of lasso; however, we demonstrate that this is not the case for models with categorical predictors. Specifically, the coding strategy used for categorical predictors impacts lasso’s performance but not linear regression. Group lasso is an alternative to lasso for models with categorical predictors. We demonstrate the inconsistency of lasso and group lasso models using a real data set: lasso performs different variable selection and has different prediction accuracy depending on the coding strategy, and group lasso performs consistent variable selection but has different prediction accuracy. Additionally, group lasso may include many predictors when very few are needed, leading to overfitting. Using Monte Carlo simulation, we show that categorical variables with one group mean differing from all others (one dominant group) are more likely to be included in the model by group lasso than lasso, leading to overfitting. This effect is strongest when the mean difference is large and there are many categories. Researchers primarily focus on the similarity between linear regression and lasso, but pay little attention to their different properties. This project demonstrates that when using lasso and group lasso, the effect of coding strategies should be considered. We conclude with recommended solutions to this issue and future directions of exploration to improve implementation of machine learning approaches in psychological science.

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Ecological Footprint Model Using the Support Vector Machine Technique

Cited by 10 publications

References 15 publications

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Ecological Footprint Prediction based on Global Macro Indicators in G-20 Countries using Machine Learning Approaches

Lasso and Group Lasso with Categorical Predictors: Impact of Coding Strategy on Variable Selection and Prediction

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