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 7 publications

(2 citation statements)

References 8 publications

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“…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

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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.

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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

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“…The next model was fitted into the residual of the present model. This sequential learning continued, until either the total training data is predicted accurately, or the most extreme number of models are added [21]. The Boosting Algorithm is a slow learner.…”

Section: 24mentioning

confidence: 99%

COVID-19 County Level Severity Classification with Imbalanced Dataset: A NearMiss Under-sampling Approach

Oladunni

Tossou

Haile

et al. 2021

Preprint

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COVID-19 pandemic that broke out in the late 2019 has spread across the globe. The disease has infected millions of people. Thousands of lives have been lost. The momentum of the disease has been slowed by the introduction of vaccine. However, some countries are still recording high number of casualties. The focus of this work is to design, develop and evaluate a machine learning county level COVID-19 severity classifier. The proposed model will predict severity of the disease in a county into low, moderate, or high. Policy makers will find the work useful in the distribution of vaccines. Four learning algorithms (two ensembles and two non-ensembles) were trained and evaluated. Class imbalance was addressed using NearMiss under-sampling of the majority classes. The result of our experiment shows that the ensemble models outperformed the non-ensemble models by a considerable margin.

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Classification of Cardiovascular Disease Using AdaBoost Method

Bazilevych

Butkevych

Padalko

2022

Smart Technologies in Urban Engineering

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

Cited by 7 publications

References 8 publications

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

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

COVID-19 County Level Severity Classification with Imbalanced Dataset: A NearMiss Under-sampling Approach

Classification of Cardiovascular Disease Using AdaBoost Method

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