Bootstrap Aggregating Multivariate Adaptive Regression Splines (Bagging MARS) to Analyse the Lecturer Research Performance in Private University

Hasyim, Maylita; Rahayu, Dwi Shinta; Muliawati, Novita Eka; Hayuhantika, Diesty; Puspasari, Ratih; Anggreini, Dewi; Hastari, Ratri Candra; Hartanto, Setyo; Utomo, Fajar Hendro

doi:10.1088/1742-6596/1114/1/012117

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bagging is used for the purpose of improve the classification accuracy of the MARS method. Thus, this study is expected to obtain better modelling and classification functions through bagging MARS method (Hasyim et al 2018).…”

Section: Methodsmentioning

confidence: 99%

Investigation of the Effects of Silage Type, Silage Consumption, Birth Type and Birth Weight on Live Weight in Kıvırcık Lambs With MARS and Bagging MARS Algorithms.

Şengül

Çelik

2021

Preprint

View full text Add to dashboard Cite

This study was carried out to determine the effect of silage type, silage consumption, birth type (single or twin) and birth weight on live weight at the end of fattening in Kıvırcık lambs. In the experiment, 40 male Kıvırcık lambs aged 2.5-3 months were used and the animals were fattened for 56 days. During the fattening period, the lambs fed with 5 different types of silage (100% sunflower silage, 75% sunflower + 25% corn silage, 50% sunflower + 50% corn silage, 25% sunflower + 75% corn silage, 100% corn silage) pure and mixed in different proportions and concentrate feed. Data on fattening results were analyzed with MARS and Bagging MARS algorithms. The main objective of this research is to predict live weight of lambs using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a nonparametric regression technique. Live weight value was modeled based on factors such as birth type, birth weight, silage type and silage consumption. Correlation coefficient (r), determination coefficient (R2), Adjust R2, Root-mean-square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error (MAPE), mean absolute deviation (MAD), and Akaike Information Criteria (AIC) values of MARS algorithm predicting live weight were as follows: 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88 respectively. Like statistics for Bagging MARS algorithm were 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47 and 115 respectively. It was observed that MARS and Bagging MARS algorithms have revealed correct results according to goodness of fit statistics. However, it has been revealed that MARS algorithm gives better results in live weight modeling.

show abstract

Section: Methodsmentioning

confidence: 99%

Investigation of the Effects of Silage Type, Silage Consumption, Birth Type and Birth Weight on Live Weight in Kıvırcık Lambs With MARS and Bagging MARS Algorithms.

Şengül

Çelik

2021

Preprint

View full text Add to dashboard Cite

show abstract

Practicability of MARS and bagging MARS algorithms in prediction of plant length of grass pea (Lathyrus sativus L.) in Turkey

Çatal,

Celik,

Bakoglu

2023

Acta Physiol Plant

View full text Add to dashboard Cite

Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field

Abbas¹,

Al‐Mudhafar²

2021

Day 1 Mon, August 16, 2021

View full text Add to dashboard Cite

Estimating rock facies from petrophysical logs in non-cored wells in complex carbonates represents a crucial task for improving reservoir characterization and field development. Thus, it most essential to identify the lithofacies that discriminate the reservoir intervals based on their flow and storage capacity. In this paper, an innovative procedure is adopted for lithofacies classification using data-driven machine learning in a well from the Mishrif carbonate reservoir in the giant Majnoon oil field, Southern Iraq. The Random Forest method was adopted for lithofacies classification using well logging data in a cored well to predict their distribution in other non-cored wells. Furthermore, three advanced statistical algorithms: Logistic Boosting Regression, Bagging Multivariate Adaptive Regression Spline, and Generalized Boosting Modeling were implemented and compared to the Random Forest approach to attain the most realistic lithofacies prediction. The dataset includes the measured discrete lithofacies distribution and the original log curves of caliper, gamma ray, neutron porosity, bulk density, sonic, deep and shallow resistivity, all available over the entire reservoir interval. Prior to applying the four classification algorithms, a random subsampling cross-validation was conducted on the dataset to produce training and testing subsets for modeling and prediction, respectively. After predicting the discrete lithofacies distribution, the Confusion Table and the Correct Classification Rate Index (CCI) were employed as further criteria to analyze and compare the effectiveness of the four classification algorithms. The results of this study revealed that Random Forest was more accurate in lithofacies classification than other techniques. It led to excellent matching between the observed and predicted discrete lithofacies through attaining 100% of CCI based on the training subset and 96.67 % of the CCI for the validating subset. Further validation of the resulting facies model was conducted by comparing each of the predicted discrete lithofacies with the available ranges of porosity and permeability obtained from the NMR log. We observed that rudist-dominated lithofacies correlates to rock with higher porosity and permeability. In contrast, the argillaceous lithofacies correlates to rocks with lower porosity and permeability. Additionally, these high-and low-ranges of permeability were later compared with the oil rate obtained from the PLT log data. It was identified that the high-and low-ranges of permeability correlate well to the high- and low-oil rate logs, respectively. In conclusion, the high quality estimation of lithofacies in non-cored intervals and wells is a crucial reservoir characterization task in order to obtain meaningful permeability-porosity relationships and capture realistic reservoir heterogeneity. The application of machine learning techniques drives down costs, provides for time-savings, and allows for uncertainty mitigation in lithofacies classification and prediction. The entire workflow was done through R, an open-source statistical computing language. It can easily be applied to other reservoirs to attain for them a similar improved overall reservoir characterization.

show abstract

Bootstrap Aggregating Multivariate Adaptive Regression Splines (Bagging MARS) to Analyse the Lecturer Research Performance in Private University

Cited by 3 publications

References 7 publications

Investigation of the Effects of Silage Type, Silage Consumption, Birth Type and Birth Weight on Live Weight in Kıvırcık Lambs With MARS and Bagging MARS Algorithms.

Investigation of the Effects of Silage Type, Silage Consumption, Birth Type and Birth Weight on Live Weight in Kıvırcık Lambs With MARS and Bagging MARS Algorithms.

Practicability of MARS and bagging MARS algorithms in prediction of plant length of grass pea (Lathyrus sativus L.) in Turkey

Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field

Contact Info

Product

Resources

About