Extra-Tree Classifier with Metaheuristics Approach for Email Classification

Sharaff, Aakanksha; Gupta, Harshil

doi:10.1007/978-981-13-6861-5_17

Cited by 131 publications

(73 citation statements)

References 11 publications

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“…Features were selected based on their relevance to the classification task that this study proposed. This was accomplished using three techniques: Pearson's Correlation, Recursive Feature Elimination (RFE) [23] and Extra Tree Classifier [24], used to estimate feature importance. The common least important features from each method were dropped from both training and testing datasets; Figure 2 illustrates this process.…”

Section: Feature Selectionmentioning

confidence: 99%

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Dalmeida

Masala

2021

Sensors

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Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence, the study will be mainly focusing on heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing.

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Section: Feature Selectionmentioning

confidence: 99%

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Dalmeida

Masala

2021

Sensors

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“…We took advantage of several algorithms, with the aim of understanding which ones work best in this specific context. In particular, we evaluated: K-nearest neighbor (KNN) [ 34 ], classification and regression tree (CART) [ 35 ], support vector machine (SVM) [ 36 ], multi-layer perceptron (MLP) [ 37 ], Ada boosting with decision tree (AB) [ 38 ], gradient boosting (GB) [ 39 ], random forest (RF) [ 40 ], and extra tree (ET) [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study

Delnevo

Mancini

Roccetti

et al. 2021

Sensors

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This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors. We have employed various machine learning algorithms, including gradient boosting and random forest, with psychological variables relative to 221 subjects to predict both the BMI values and the BMI status (normal, overweight, and obese) of those subjects. We have found that the psychological variables in use allow one to predict both the BMI values (with a mean absolute error of 5.27–5.50) and the BMI status with an accuracy of over 80% (metric: F1-score). Further, our study has also confirmed the particular efficacy of psychological variables of negative type, such as depression for example, compared to positive ones, to achieve excellent predictive BMI values.

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“…It handles the data missing values efficiently. [24] ETC Extra trees classifier working is quite similar to the random forest and only different from it in method of construction of trees in the forest. Every decision tree in the extra tree classifier is made from the original training sample.…”

Section: E Evaluation Matricesmentioning

confidence: 99%

“…This motivated our attempts to help healthcare professionals by developing machine learning techniques in the diagnosis of CVD patients' survival. We employed nine machine learning models: Decision Tree (DT) [18], Adaptive Boosting model (AdaBoost) [19], Logistic Regression (LR) [20], Stochastic Gradient Descent (SGD) [21], Random Forest (RF) [22], Gradient Boosting classifier (GBM) [23], Extra Tree Classifier (ETC) [24], Gaussian Naive Bayes (G-NB) [25] and Support Vector Machine (SVM) [26]. Synthetic Minority Oversampling Technique (SMOTE) is applied to handle class-imbalance problem.…”

Section: Introductionmentioning

confidence: 99%

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

et al. 2021

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Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient's survivor prediction. To predict patient's survival, this study applies nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient's survival.

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Extra-Tree Classifier with Metaheuristics Approach for Email Classification

Cited by 131 publications

References 11 publications

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

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