scholarly journals Student Profile Modeling Using Boosting Algorithms

2022 ◽  
Vol 17 (5) ◽  
pp. 1-13
Author(s):  
Touria Hamim ◽  
Faouzia Benabbou ◽  
Nawal Sael
Keyword(s):  
Student Performance ◽  
Information Gain ◽  
Recursive Feature Elimination ◽  
Gradient Boosting ◽  
Fisher Score ◽  
Student Profile ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Boosting Algorithms ◽  
Classification Prediction

The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.

scholarly journals Boosting algorithms for prediction in agriculture: an application of feature importance and feature selection boosting algorithms for prediction crop damage.

2021 ◽  
Author(s):  
Viviane Costa Silva ◽  
Mateus Silva Rocha ◽  
Glaucia Amorim Faria ◽  
Silvio Fernando Alves Xavier Junior ◽  
Tiago Almeida de Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Crop Damage ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Agriculture Sector ◽  
Light Gradient ◽  
Learning Techniques ◽  
Gradient Boosting Machine ◽  
Boosting Algorithms

Abstract The Agriculture sector has created and collected large amounts of data. It can be gathered, stored, and analyzed to assist in decision making generating competitive value, and the use of Machine Learning techniques has been very effective for this market. In this work, a Machine Learning study was carried out using supervised classification models based on boosting to predict disease in a crop, thus identifying the model with the best areas under curve metrics. Light Gradient Boosting Machine, CatBoost Classifier, Extreme Gradient, Gradient Boosting Classifier, Adaboost models were used to qualify the crop as healthy or sick. One can see that the LightGBM algorithm provided a better fit to the data with an area under the curve of 0.76 under the use of BORUTA variable selection.

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

Development of a Diabetes Melitus Detection and Prediction Model Using Light Gradient Boosting Machine and K-Nearest Neighbour

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
B. A Omodunbi
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Hybrid Model ◽  
Learning Model ◽  
Experimental Result ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Machine Learning Model ◽  
Gradient Boosting Machine ◽  
Receiver Operating

Diabetes mellitus is a health disorder that occurs when the blood sugar level becomes extremely high due to body resistance in producing the required amount of insulin. The aliment happens to be among the major causes of death in Nigeria and the world at large. This study was carried out to detect diabetes mellitus by developing a hybrid model that comprises of two machine learning model namely Light Gradient Boosting Machine (LGBM) and K-Nearest Neighbor (KNN). This research is aimed at developing a machine learning model for detecting the occurrence of diabetes in patients. The performance metrics employed in evaluating the finding for this study are Receiver Operating Characteristics (ROC) Curve, Five-fold Cross-validation, precision, and accuracy score. The proposed system had an accuracy of 91% and the area under the Receiver Operating Characteristic Curve was 93%. The experimental result shows that the prediction accuracy of the hybrid model is better than traditional machine learning

scholarly journals Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

2020 ◽  
Vol 12 (14) ◽  
pp. 2271 ◽  
Author(s):  
Jinwoong Park ◽  
Jihoon Moon ◽  
Seungmin Jung ◽  
Eenjun Hwang
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Jeju Island ◽  
Gradient Boosting ◽  
Probabilistic Forecasting ◽  
Training Time ◽  
Light Gradient ◽  
Proposed Model ◽  
Gradient Boosting Machine ◽  
Time Problem

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.

scholarly journals Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150960-150968 ◽  
Author(s):  
Tingting Chen ◽  
Jun Xu ◽  
Haochao Ying ◽  
Xiaojun Chen ◽  
Ruiwei Feng ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Extubation Failure ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Gradient Boosting Machine

scholarly journals Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

2020 ◽  
Vol 17 (3) ◽  
pp. 1082 ◽  
Author(s):  
Saifur Rahman ◽  
Muhammad Irfan ◽  
Mohsin Raza ◽  
Khawaja Moyeezullah Ghori ◽  
Shumayla Yaqoob ◽  
...  
Keyword(s):  
Physical Activity ◽  
Performance Analysis ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Method Performance ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithms

Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing.

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