Supervised Ensemble Machine Learning Aided Performance Evaluation of Sentiment Classification

Rahman, Sheikh Shah Mohammad Motiur; Rahman, Md. Habibur; Sarker, Kaushik; Rahman, Md. Samadur; Ahsan, Nazmul; Sarker, M. Mesbahuddin

doi:10.1088/1742-6596/1060/1/012036

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…Precision measures correct positive predictions from total positive predictions by model. Sensitivity (Recall)gives value of c orrect positive perditions from actual positive cases in inputs.F1 Score attributes the harmonic mean (average) of the precision and recall (Rahman et al , 2018; Rajit and Amit, 2020). F1 score is the entity that includes both recall and precision of the system.…”

Section: Methodsmentioning

confidence: 99%

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Chauhan

Modi²,

Shrivastava

2021

WJE

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Purpose The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making. Design/methodology/approach In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score. Findings The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset. Originality/value Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

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Section: Methodsmentioning

confidence: 99%

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Chauhan

Modi²,

Shrivastava

2021

WJE

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“…ETC is an ensemble learning approach that combines the results of multiple decision trees to obtain the classification results [62]. The difference between ETC and RF is the manner of constructing the decision trees in the forest.…”

Section: Extra Tree Classifiermentioning

confidence: 99%

Denial of Service Attack Classification Using Machine Learning with Multi-Features

et al. 2022

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The exploitation of internet networks through denial of services (DoS) attacks has experienced a continuous surge over the past few years. Despite the development of advanced intrusion detection and protection systems, network security remains a challenging problem and necessitates the development of efficient and effective defense mechanisms to detect these threats. This research proposes a machine learning-based framework to detect distributed DOS (DDoS)/DoS attacks. For this purpose, a large dataset containing the network traffic of the application layer is utilized. A novel multi-feature approach is proposed where the principal component analysis (PCA) features and singular value decomposition (SVD) features are combined to obtain higher performance. The validation of the multi-feature approach is determined by extensive experiments using several machine learning models. The performance of machine learning models is evaluated for each class of attack and results are discussed regarding the accuracy, recall, and F1 score, etc., in the context of recent state-of-the-art approaches. Experimental results confirm that using multi-feature increases the performance and RF obtains a 100% accuracy.

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FakeTouch: Machine Learning Based Framework for Detecting Fake News

Siddikk

Lia

Muntasir

et al. 2022

Big Data Intelligence for Smart Applications

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Supervised Ensemble Machine Learning Aided Performance Evaluation of Sentiment Classification

Cited by 11 publications

References 11 publications

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis

Denial of Service Attack Classification Using Machine Learning with Multi-Features

FakeTouch: Machine Learning Based Framework for Detecting Fake News

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