A Review of Light Gradient Boosting Machine Method for Hate Speech Classification on Twitter

Abdurrahman, Muhammad Hafizh; Irawan, Budhi; Setianingsih, Casi

doi:10.1109/icecie50279.2020.9309565

Cited by 15 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LightGBM, short for Light Gradient Boosting Machine, is a gradient-boosting framework that uses tree-based learning algorithms. Proposed by [24] to solve the issue of the Gradient Boosting Decision Tree (GBDT) in conventional implementations, it is based on two novel techniques, Gradient-Based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) [49]. In [24], the experiment demonstrated that LigthGBM can accelerate the training process by up to over 20 times.…”

Section: Overview Of Machine Learning Modelsmentioning

confidence: 99%

Proposal of a Machine Learning Approach for Traffic Flow Prediction

Berlotti,

Di Grande,

Cavalieri

2024

Sensors

View full text Add to dashboard Cite

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered.

show abstract

Section: Overview Of Machine Learning Modelsmentioning

confidence: 99%

Proposal of a Machine Learning Approach for Traffic Flow Prediction

Berlotti,

Di Grande,

Cavalieri

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The LightGBM method in classifying hate speech consisted of one thousand data samples, with 70% of the dataset being training data and 30% being evaluation data. An accuracy of 86.05% was achieved [12]. Aishwarya Mujumdar and Dr. Vaidehi V. (2019) created a pipelined model for diabetes classi cation and prediction.…”

Section: Related Workmentioning

confidence: 99%

Boosting-based machine learning approaches for diabetes prediction using Indian demographic and health survey-2021 data

Thotad

Bharamagoudar

Kallur

2023

Preprint

View full text Add to dashboard Cite

This study examines the effectiveness of boosting-based machine learning classifiers in identifying diabetes in the Indian population. Traditional diagnostic methods for diabetes are time-consuming and prone to errors, and computer-aided diagnostic techniques can improve performance and reduce costs. The study analyzes data from the Indian Demographic and Health Survey 2021, focusing on women aged 19–49 who tested positive for diabetes. The dataset includes clinical, anthropometric, and biochemical components, and 12,103 positive responders was considered. The study adopts predictive exploration-based boosting machine learning models, including adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Feature extraction is performed using kernel principal component analysis. The extreme gradient boosting model performs well on the dataset, with accuracy, f1-score, precision, and recall values of 81% and 83%, 81% and 82%, 81% and 88%, and 81% and 76%, respectively, before and after applying kernel principal component analysis. Adaptive boosting performs poorly, while categorical, gradient, and light gradient boosting models perform moderately. The study achieves maximum accuracy, precision, the area under the curve, and recall.

show abstract

“…A different method develops trees parallel to the ground, whereas LGBM produces trees upwardly, or, to put it another way, LGBM produces trees leaf by leaf, whereas another method produces trees level by level. The leaf with the greatest delta erosion will be produced [32]. This study used the default parameter for LGBM classifiers.…”

Section: Lightgbm Classifiermentioning

confidence: 99%

Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics

Rahman,

Abdulrazak,

Ali

et al. 2023

Algorithms

View full text Add to dashboard Cite

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

show abstract

A Review of Light Gradient Boosting Machine Method for Hate Speech Classification on Twitter

Cited by 15 publications

References 16 publications

Proposal of a Machine Learning Approach for Traffic Flow Prediction

Proposal of a Machine Learning Approach for Traffic Flow Prediction

Boosting-based machine learning approaches for diabetes prediction using Indian demographic and health survey-2021 data

Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics

Contact Info

Product

Resources

About