Ensemble Based Approach for Intrusion Detection Using Extra Tree Classifier

Bhati, Bhoopesh Singh; S., C.

doi:10.1007/978-981-15-2780-7_25

Cited by 32 publications

(11 citation statements)

References 13 publications

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“…Extra-Trees classifier randomly selects specific decisions and subsets of data in order to avoid overlearning and overfitting. The Extra-Trees classifier is a decision tree-based ensemble learning approach ( Bhati & Rai, 2020 ). Extra-Trees classifier randomly selects specific decisions and subsets of data in order to avoid overlearning and overfitting.…”

Section: Related Studymentioning

confidence: 99%

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Islam

Haque

Miah

et al. 2022

PeerJ Computer Science

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Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine’s machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that the ANN Classifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.

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Section: Related Studymentioning

confidence: 99%

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Islam

Haque

Miah

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

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“…For calculating the importance feature X m for predicting Y in a tree structure T by summing up the decrease in the weighted impurity (p t )Δ i(S t, t) for all nodes t, where feature X m is used, then averaging over all N t trees in the forest.

where p ( t ) is the proportion of N N T samples reaching node t and v ( S t ) is the feature used in split S t ( Bhati and Rai, 2020 ). The decrease in some impurity measures i ( t ) at node t is represented by the following formula:

Where, p L = Nt/N , p R = NtR/N and split st = s * for which the partition of the N node samples into two subsets t L and t R uplift the decrease in the impurity is identified.…”

Section: Methodsmentioning

confidence: 99%

Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology

Rustam

Reshi

Aljedaani

et al. 2022

Saudi Journal of Biological Sciences

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Every year about one million people die due to diseases transmitted by mosquitoes. The infection is transmitted to a person when an infected mosquito stings, injecting the saliva into the human body. The best possible way to prevent a mosquito-borne infection till date is to save the humans from exposure to mosquito bites. This study proposes a Machine Learning (ML) and Deep Learning based system to detect the presence of two critical disease spreading classes of mosquitoes such as the Aedes and Culex. The proposed system will effectively aid in epidemiology to design evidence-based policies and decisions by analyzing the risks and transmission. The study proposes an effective methodology for the classification of mosquitoes using ML and CNN models. The novel RIFS has been introduced which integrates two types of feature selection techniques – the ROI-based image filtering and the wrappers-based FFS technique. Comparative analysis of various ML and deep learning models has been performed to determine the most appropriate model applicable based on their performance metrics as well as computational needs. Results prove that ETC outperformed among the all applied ML model by providing 0.992 accuracy while VVG16 has outperformed other CNN models by giving 0.986 of accuracy.

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“…The splits for each of the max_features are drawn. Another parameter max_depth is implemented with the value of 300 which indicated the maximum depth of each tree in the forest [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

et al. 2022

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COVID-19 vaccination raised serious concerns among the public and people are mind stuck by various rumors regarding the resulting illness, adverse reactions, and death. Such rumors are dangerous to the campaign against the COVID-19 and should be dealt with accordingly and timely. One prospective solution is to use machine learning-based models to predict the death risk for vaccinated people and clarify people’s perceptions regarding death risk. This study focuses on the prediction of the death risks associated with vaccinated people followed by a second dose for two reasons; first to build consensus among people to get the vaccines; second, to reduce the fear regarding vaccines. Given that, this study utilizes the COVID-19 VAERS dataset that records adverse events after COVID-19 vaccination as ‘recovered’, ‘not recovered’, and ‘survived’. To obtain better prediction results, a novel voting classifier extreme regression-voting classifier (ER-VC) is introduced. ER-VC ensembles extra tree classifier and logistic regression using soft voting criterion. To avoid model overfitting and get better results, two data balancing techniques synthetic minority oversampling (SMOTE) and adaptive synthetic sampling (ADASYN) have been applied. Moreover, three feature extraction techniques term frequency-inverse document frequency (TF-IDF), bag of words (BoW), and global vectors (GloVe) have been used for comparison. Both machine learning and deep learning models are deployed for experiments. Results obtained from extensive experiments reveal that the proposed model in combination with TF-TDF has shown robust results with a 0.85 accuracy when trained on the SMOTE-balanced dataset. In line with this, validation of the proposed voting classifier on binary classification shows state-of-the-art results with a 0.98 accuracy. Results show that machine learning models can predict the death risk with high accuracy and can assist the authors in taking timely measures.

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Ensemble Based Approach for Intrusion Detection Using Extra Tree Classifier

Cited by 32 publications

References 13 publications

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Vector mosquito image classification using novel RIFS feature selection and machine learning models for disease epidemiology

Novel extreme regression-voting classifier to predict death risk in vaccinated people using VAERS data

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