Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification

Dutta, J.; Kim, Yong Woon; Dominic, Dalia

doi:10.1109/icrcicn50933.2020.9296176

Cited by 9 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial data set may have abnormalities or inaccurate values that impair the quality of the dataset. [12] Data pre-processing involves various steps such as Data cleaning, Encoding Categorical values, feature selection, Data normalization or standardization and Training and Testing.…”

Section: Data Pre-processing and Experimental Setupmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Auto Insurance Fraud Detection

2024

IRJMETS

View full text Add to dashboard Cite

Auto insurance fraud is a significant problem in the insurance industry, with losses estimated to be in the billions of dollars annually. Insurance fraud can take many forms, including staged accidents, false injury claims, and exaggerated damage claims. The impact of auto insurance fraud is significant for both insurance companies and policyholders. Insurance fraud results in higher premiums for policyholders, as insurance companies pass on the costs of fraudulent claims to their customers. Fraudulent claims can also result in delays in claims processing and payouts, as insurance companies must investigate claims to determine their validity. Goal is to create a solution that analyzes auto insurance claim data to identify cases of fraud claims. Using machine learning algorithms, models are built to classify these claims. Also, This study compares the average accuracy, precision, recall, and other characteristics of all classification based machine learning methods. A machine learning model is created for fraudulent transaction validation using the scikit-learn Python Library.

show abstract

Section: Data Pre-processing and Experimental Setupmentioning

confidence: 99%

Comparative Analysis of Machine Learning Models for Auto Insurance Fraud Detection

2024

IRJMETS

View full text Add to dashboard Cite

show abstract

“…3. The proposed algorithm is compared with logistic regression (LR) [54], support vector classifier (SVC) [54], random forest (RF) classifier [55], Ada boost classifier [55], K-nearest neighbor (KNN) [56], extreme gradient boosting (XGB) [57], extra trees classifier [58], gradient boosting classifier [59], and Gaussian NB [60]. These classifiers are conducted based on two and three classes for measuring the performance in each one.…”

Section: Proposed Algorithms For Multi-weight Polarity Sentimentsmentioning

confidence: 99%

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

show abstract

“…It involves training the models on the preprocessed healthcare data to learn patterns, relationships, and predictive features associated with different diseases. Various machine learning algorithms, such as XGBoost (eXtreme Gradient Boosting), can be employed for this purpose [5]. The training process involves splitting the dataset into training and validation subsets to assess model performance and prevent overfitting.…”

Section: Model Trainingmentioning

confidence: 99%

“…XGBoost is a powerful ensemble learning algorithm that is used for both classification and regression tasks [6]. It uses a gradient boosting framework to iteratively train multiple weak models (decision trees) on the data, where each subsequent model tries to correct the errors of the previous models [5].…”

Section: Xgboostmentioning

confidence: 99%

A Healthcare System using Machine Learning Techniques for Disease Prediction with Chatbot Assistance

Badempet,

Cheerala,

Anagondi

2023

Preprint

View full text Add to dashboard Cite

This project aims to develop a healthcare system that can predict multiple diseases, including liver disease, diabetes and heart disease using machine learning techniques. These are the most common types of Diseases that plaque the Indians today. This system will use XGBoost (eXtreme Gradient Boosting) for these diseases while maintaining accuracy, speed, resource utilization, stability and reducing complexity. The system will also include a healthcare chatbot that can provide personalized health recommendations to users. The system will be developed using Python and its many Machine Learning Libraries & Streamlit Framework. Machine learning models will be trained on publicly available datasets to predict diseases accurately. The developed models will be evaluated using performance metric like accuracy of training and testing data. The healthcare chatbot will be developed with NLP and/or OpenAI API Integration Techniques to provide personalized health recommendations to users. The system will be deployed on a cloud-based platform to ensure scalability and availability. The Healthcare System has the potential to improve the accuracy of disease diagnosis, care and early intervention, thereby reducing healthcare costs and improving patient outcomes. The healthcare chatbot can provide accessible and personalized health recommendations, leading to a healthier and happier life.

show abstract

Comparison of Gradient Boosting and Extreme Boosting Ensemble Methods for Webpage Classification

Cited by 9 publications

References 8 publications

Comparative Analysis of Machine Learning Models for Auto Insurance Fraud Detection

Comparative Analysis of Machine Learning Models for Auto Insurance Fraud Detection

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

A Healthcare System using Machine Learning Techniques for Disease Prediction with Chatbot Assistance

Contact Info

Product

Resources

About