Extra Trees Classifier Research Articles

Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India

Remote sensing (RS), Geographic information systems (GIS), and Machine learning can be integrated to predict land surface temperatures (LST) based on the data related to carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), absorbing aerosol index (AAI), and Aerosol optical depth (AOD). In this study, LST was predicted using machine learning classifiers, i.e., Extra trees classifier (ET), Logistic regressors (LR), and Random Forests (RF). The accuracy of the LR classifier (0.89 or 89%) is higher than ET (82%) and RF (82%) classifiers. Evaluation metrics for each classifier are presented in the form of accuracy, Area under the curve (AUC), Recall, Precision, F1 score, Kappa, and MCC (Matthew’s correlation coefficient). Based on the relative performance of the ML classifiers, it was concluded that the LR classifier performed better. Geographic information systems and RS tools were used to extract the data across spatial and temporal scales (2019 to 2022). In order to evaluate the model graphically, ROC (Receiver operating characteristic) curve, Confusion matrix, Validation curve, Classification report, Feature importance plot, and t- SNE (t-distributed stochastic neighbour embedding) plot were used. On validation of each ML classifier, it was observed that the RF classifier returned model complexity due to limited data availability and other factors yet to be studied post data availability. Sentinel-5-P and MODIS data are used in this study.

Prediction of viral families and hosts of single-stranded RNA viruses based on K-Mer coding from phylogenetic gene sequences

There are billions of virus species worldwide, and viruses, the smallest parasitic entities, pose a serious threat. Therefore, fighting associated disorders requires an understanding of the genetic structure of viruses. Considering the wide diversity and rapid evolution of viruses, there is a critical need to quickly and accurately classify viral species and their potential hosts to better understand transmission dynamics, facilitating the development of targeted therapies. Recognizing this, this study has investigated the classes of RNA viruses based on their genomic sequences using Machine Learning (ML) and Deep Learning (DL) models. The PhyVirus dataset, consisting of pathogenic Single-stranded RNA viruses of Baltimore group four (+ssRNA) and five (-ssRNA) with different hosts and species, was analyzed. The dataset containing viral gene sequences was analyzed using the K-Mer coding technique, which is based on base words of various lengths. The study used classical ML algorithms (Random Forest, Gradient Boosting and Extra Trees) and the Fully Connected Deep Neural Network, a Deep Learning algorithm, to predict viral families and hosts. Detailed analyses were performed on the classifier performance in scenarios with different train-test ratios and different word lengths (k-values) for K-Mer. The observed results show that Fully Connected Deep Neural Network has a high success rate of 99.60 % in predicting virus families. In predicting virus hosts, the Extra Trees classifier achieved the highest success rate of 81.53 %. This study is considered to be the first classification study in the literature on this dataset, which has a very large family and host diversity consisting of gene sequences of Single-stranded RNA viruses. Our detailed investigations on how varying word lengths based on K-Mer coding in gene sequences affect the classification into viral families and hosts make this study particularly valuable. This study shows that ML and DL methods have the potential to produce valuable results in phylogenetic studies. In addition, the results and high-performance values show that these methods can be successfully used in regenerative applications of gene sequences or in studies such as the elimination of losses in gene sequences.

A secure and light‐weight patient survival prediction in Internet of Medical Things framework

SummaryThoracic surgeries in major lung resections for primary lung cancer are fraught with potential risks, emphasising the need to understand factors contributing to postoperative mortality. This study investigates the interplay of objective and subjective data in predicting postoperative outcomes to reduce data transmission costs in the Internet of Medical Things (IoMT). Objective metrics, such as forced vital capacity (FVC), offer consistent, quantifiable insights essential for predictive modelling. Conversely, subjective data derived from patient self‐reports suggest that the patient's personal experiences are crucial for assessing the quality of life postsurgery. Utilising a dataset from the University of California, Irvine's Machine Learning Repository (UCI), 17 distinct attributes were examined. Using ensemble learning classifiers, the extra trees classifier is superior when utilising all features, achieving an accuracy of 0.92. Combining select subjective features, specifically PRE6, PRE8 and AGE (demographic), with objective data, yielded a comparable accuracy of 0.91. Feature importance analysis further highlights the significance of features like PRE5, PRE4 and AGE. This suggests potential redundancies in the full feature set, emphasising the importance of feature selection. Importantly, when compared with existing literature, this study's findings offer insights into the future of predictive modelling in thoracic surgeries, with implications for the rapidly evolving field of the IoMT.

DDoS SourceTracer: An Intelligent Application for DDoS Attack Mitigation in SDN

Software-Defined Networking (SDN) enhances network management and efficiency and is particularly effective in defending against Distributed Denial of Service (DDoS) attack through its centralized structure. Our proposed DDoS SourceTracer Application utilizes SDN to efficiently identify and mitigate DDoS attack by employing tracebacking and clustering techniques. This application uses supervised and ensemble machine learning algorithms for attack detection, and feature selection methods like the Chi-square test, ANOVA (Analysis of Variance) F-test, Correlation matrix, and Extra tree classifier to optimize the feature set. Our results show that the clustering approach outperforms traditional methods like rate limiting and blocking and effectively mitigates the attack in just 3.5 s. We used the sFlow-RT tool on the Zoo topology to perform real-time analysis and validate our application’s effectiveness during attack and normal traffic. This tool analyzes how attack traffic is impacted when using clustering and tracebacking methods to mitigate DDoS attack.

Predicting the Risk of Diabetes and Heart Disease with Machine Learning Classifiers: The Mediation Analysis

ABSTRACT Purpose This research employs machine learning and mediation analysis, along with path analysis, to investigate the correlations between factors such as body mass index (BMI) and the occurrence of diabetes and heart disease among the Indian population. The objective is to enhance models that are specifically designed to accommodate lifestyles, genetic differences, and healthcare obstacles. Methods Our research combines a range of data that includes aspects such as lifestyle, physical health, and mental well-being. We use mediation and path analysis techniques to identify the factors involved in the process, while also utilizing machine learning classifiers to enhance risk assessment. In addition to considering known risks, we also investigate biomarkers. Incorporate time factors through analyses. Results Mediation and path models analyze that diabetes and heart disease are partially mediated with their coefficients a = 7.85, b = 0.01, and c-c’ = 0.10. In the path analysis model, the standardized values of exposure and outcome variables are 4.14 and 6.85, respectively, showing a significant relationship with the mediator and other covariates. In classification, the Random Forest classifier shows 99% accuracy and precession, while the Decision Tree, Extra Tree, K-Nearest, and Adaboost classifiers have an accuracy of 98%, 97%, 96%, and 95%, which shows that the machine learning classifiers are more significant for the study. Conclusion This study contributes to the development of risk management for diabetes and heart disease in India by utilizing machine learning and mediation analysis. It examines relationships, such as BMI, to provide insights for targeted measures, thereby contributing to global discussions on health.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

BackgroundRadiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals.MethodsDosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 − (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 − and AST 2 + groups and cutoff value calculations.ResultsResults showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71–0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models.ConclusionsThis study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Extra Tree Classifier Predicts an Interactome Hub Gene as HSPB1 in Oral Cancer: A Bioinformatics Analysis

Extra Tree Classifier Predicts an Interactome Hub Gene as HSPB1 in Oral Cancer: A Bioinformatics Analysis

BALANCING SARCASTIC HINGLISH SHORT TEXT DATA USING AUGMENTATION TECHNIQUES WITH HANDLING SPELLING VARIATIONS

In the real world, there is a significant presence of imbalanced data due to the fact that the classes that make up the datasets are not evenly distributed. Even when using methods that are traditionally used to achieve class balance, such as re-sampling &amp; re-weighting, current deep learning still faces a significant obstacle because of the class imbalance. This study’s major objective is proposing a data augmentation technique to balance the data to improve the sample sizes for the minority classes. Python, a well-known programming language, &amp; multiple methods of machine learning are being employed in the execution of this study. Classification models like Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra Trees Classifier, AdaBoost classifier, Gradient Boost classifier was used to implement this study. Precision, recall, &amp; F-score were used to determine which model would be the most effective. According to the findings of this study's analysis, the Naive Bayes approach, which has a F1-Score of 95.85% &amp; has Wn = 3, Cn = 3, &amp; CWn =3 as its parameters, is the technique that yields the most accurate results.

Stress Detection in IT Employees using Machine Learning

In today's fast-paced technology landscape, stress management is becoming increasingly important, especially among IT professionals. The work environment in the IT industry is often characterized by long hours, tight deadlines, and high expectations, which can lead to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects productivity and job satisfaction. This study aims to predict the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a comprehensive view of both the physiological and work-related factors that contribute to stress. The application of machine learning in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to provide actionable insights for both individuals and organizations. Individuals can use these predictions for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using machine learning for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health and well-being in the workplace Key Words: Random Forest , AdaBoost Classifier, Extra Tree Classifier, Decision Tree, Stacking etc.

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches

This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-silico prediction. Addressing this gap, the study seeks to develop an efficient computational model for plant stress response prediction. The two benchmark datasets for MiRNA and Pre-MiRNA dataset have been acquired in this study. Four ensemble approaches such as bagging, boosting, stacking, and blending have been employed. Classifiers such as Random Forest (RF), Extra Trees (ET), Ada Boost (ADB), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM). Stacking and Blending employed all stated classifiers as base learners and Logistic Regression (LR) as Meta Classifier. There have been a total of four types of testing used, including independent set, self-consistency, cross-validation with 5 and 10 folds, and jackknife. This study has utilized evaluation metrics such as accuracy score, specificity, sensitivity, Mathew's correlation coefficient (MCC), and AUC. Our proposed methodology has outperformed existing state of the art study in both datasets based on independent set testing. The SVM-based approach has exhibited accuracy score of 0.659 for the MiRNA dataset, which is better than the previous study. The ET classifier has surpassed the accuracy of Pre-MiRNA dataset as compared to the existing benchmark study, achieving an impressive score of 0.67. The proposed method can be used in future research to predict abiotic stresses in plants.

Team Performance Indicators That Predict Match Outcome in Rugby Union

The aim of the study is to identify the most significant indicators of the national team's performance at the European Rugby Championships 15 and to design a model for predicting the outcomes of matches. Data was collected from teams’ performance at the European Rugby 15 Championships 2021, 2022 and 2023 for the analysis. The total number of matches was 41. All indicators presented in the official reports were taken: 22 for the home and away teams. The analysis of the team results was carried out according to all indicators: mean value, standard deviation, and test were used to compare the performance indicators of the winning and losing teams. Machine learning techniques were utilized to develop a predictive model for match outcomes. On one hand, 15 indicators (68.2%) are higher for teams that won (winning teams). On the other hand, 7 (31.8%) indicators are higher for teams that lost. The difference between the teams' means varies from -56.46% (the minus indicates that this indicator is higher for the teams that lost) to 273.68%. Based on the results, the Random Forest Classifier and Extra Trees Classifier algorithms have the best prediction accuracy (0.92). The most significant indicators of team performance that affect the final result of the match are tries (196.3% – the difference between the average values of winning and losing teams), conversions (176.7%), missed tackles (- 56.46%), offload (126.3%). Based on the data obtained, refining the team training process in Rugby 15 is possible.

Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer

Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.

ON THE ANALYTICAL STUDY OF THE SERVICE QUALITY OF INDIAN RAILWAYS UNDER SOFT-COMPUTING PARADIGM

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 Machine Learning (ML) model estimators: Extra Trees Classifier (ETC), Support Vector Machine Classifier (SVMC) and Multinomial Naive Bayes Classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

Comparative Analysis of Machine Learning Models for Predicting Diabetes: Unveiling the Superiority of Advanced Ensemble Methods

This study presents a comprehensive analysis of various machine learning models to predict diabetes. The research evaluates and compares the predictive performance of advanced ensemble techniques Extra Trees Classifier and LightGBM with traditional machine learning algorithms and simpler deep neural network (DNN) architectures. The dataset comprises numerous features pertinent to diabetes diagnosis, such as glucose concentration, BMI, and insulin levels, among others. A methodology, including polynomial feature transformation and ten-fold cross-validation, was employed to ensure the study's reliability and the models' capability to generalize. The advanced ensemble models, Extra Trees and LightGBM, achieved stellar predictive metrics, with the former attaining a near-perfect ROC AUC, accuracy, precision, and an F1 score close to 1. LightGBM followed closely, demonstrating the high efficacy of ensemble methods in complex data settings. These results were contrasted with significantly lower performance metrics from DNNs and respectable, albeit lower, scores from traditional models like Decision Trees, Random Forest, KNN, and XGBoost.

Did disruptive events affect the purchase of private label food products?

PurposeThis paper aims to examine the impact of the COVID-19 pandemic on the private label food products, specifically assessing the stability and changes in factors influencing purchasing decisions, and comparing pre-pandemic and post-pandemic datasets.Design/methodology/approachThe study employs the Extra Tree Classifier method, a robust quantitative approach, to analyse data collected from questionnaires distributed among two distinct consumer samples. This methodological choice is explicitly adopted to provide a clear classification of factors influencing consumer preferences for private label products, surpassing conventional qualitative methods.FindingsDespite the profound disruptions caused by the COVID-19 pandemic, this research underscores the persistent hierarchy of factors shaping consumer choices in the private label food market, showing an overall stability in consumer behaviour. At the same time, the analysis of individual variables highlights the positive increase in those related to product quality, health, taste, and communication.Research limitations/implicationsThe use of online surveys for data collection may introduce a self-selection bias, and the non-probabilistic sampling method could limit the generalizability of the results.Practical implicationsPractical implications suggest that managers in the private label industry should prioritize enhancing quality control, ensuring effective communication, and dynamically adapting strategies to meet evolving consumer preferences, with a particular emphasis on quality and health attributes.Originality/valueThis study contributes to the existing body of literature by providing insights into the profound transformations induced by the COVID-19 pandemic on consumer behaviour, specifically in relation to their preferences for private label food products.

EEG based automated detection of seizure using machine learning approach and traditional features

The detection of epileptic seizures is key for neurologists to initiate the right treatment at the earliest. However, the traditional methods are dependent on manual diagnosis which are error-prone. Hence, the current article presents an automated machine learning-based approach with competing results to detect seizures and its types using traditional features. Towards this goal, the present paper explores the performance of two fractal parameters namely Higuchi and, Kat’z fractal dimension, along with power spectral density and spectral entropy to detect EEG signals with seizure and non-seizure characteristics. To validate the performance via cross-validation technique, the features are extracted and experimented from three publically available databases such as Bern-Barcelona (focal, non-focal), Khas (preictal, interictal, ictal), and Bonn. Thereafter evaluated with different machine learning algorithms like Random Forest classifier, Ada Boost classifier, Gradient Boosting classifier, Extra Tree classifier, SVM classifier, and Naive Bayes classifier. The experiments showed 100% F1 score and accuracy in classifying focal and non-focal signals for random forest and extra tree classifier methods. To the best of our knowledge, this is the first paper reporting this score for the Bern-Barcelona dataset. The same features are also experimented for the detection of interictal, ictal and preictal scenarios. In the process, 100% accuracy is obtained in classifying interictal and ictal EEG signals, and an accuracy of 94% is attained in classifying interictal-preictal signals. This score is 14% higher than the current state-of-the-art methods using the same database. The features are also used in the Bonn database, where the proposed approach gave an accuracy of 100% same as the state-of-the-art methods.

Early Detection: Machine Learning Techniques in Pancreatic Cancer Diagnosis

Pancreatic cancer is a malignant tumor that poses a significant threat to patients' lives. Malignant growth is the abnormal development of cell tissue. Pancreatic illness is one of the most obvious causes of mortality across the world. Pancreatic malignant development begins in the pancreatic tissues. The pancreas secretes proteins that aid in digestion as well as hormones that direct sugar breakdown. Pancreatic cancer is typically identified in its late stages, spreads quickly, and has a terrible prognosis. Biomarkers are critical in the management of patients with invasive malignancies. Pancreatic Ductal Adenocarcinoma has a dismal prognosis due to its advanced appearance and limited treatment choices. This is compounded by the lack of validated screening and predicting biomarkers for early detection and precision therapy, respectively. In this paper, we have attempted to discuss various Machine Learning methods to detect pancreatic cancer. The selected. urinary biomarkers values are provided as the input of Support Vector Machine (SVM), Extra Tree Classifier (ETC), Decision Tree (DT), and Random Forest (RF) methods. The diagnosing accuracy of pancreatic cancer using SVM, ETC, DT, and RF classifiers are 50, 82.16, 81.03, and 86 respectively. The experimental results prove that the Random Forest classifier is more feasible and promising for clinical applications for the diagnosis of pancreatic cancer when compared to ETC, DT, and SVM.

Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework

Background:Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded encouraging outcomes, thereby creating a hopeful therapeutic avenue for the treatment of cancer. Therefore, correctly identifying the anti-angiogenic peptides is extremely important in comprehending their biophysical and biochemical traits, laying the groundwork for uncovering novel drugs to combat cancer. Methods:In this work, we present a novel ensemble-learning-based model, Stack-AAgP, specifically designed for the accurate identification and interpretation of anti-angiogenic peptides (AAPs). Initially, a feature representation approach is employed, generating 24 baseline models through six machine learning algorithms (random forest [RF], extra tree classifier [ETC], extreme gradient boosting [XGB], light gradient boosting machine [LGBM], CatBoost, and SVM) and four feature encodings (pseudo-amino acid composition [PAAC], amphiphilic pseudo-amino acid composition [APAAC], composition of k-spaced amino acid pairs [CKSAAP], and quasi-sequence-order [QSOrder]). Subsequently, the output (predicted probabilities) from 24 baseline models was inputted into the same six machine-learning classifiers to generate their respective meta-classifiers. Finally, the meta-classifiers were stacked together using the ensemble-learning framework to construct the final predictive model. Results:Findings from the independent test demonstrate that Stack-AAgP outperforms the state-of-the-art methods by a considerable margin. Systematic experiments were conducted to assess the influence of hyperparameters on the proposed model. Our model, Stack-AAgP, was evaluated on the independent NT15 dataset, revealing superiority over existing predictors with an accuracy improvement ranging from 5% to 7.5% and an increase in Matthews Correlation Coefficient (MCC) from 7.2% to 12.2%.

Progressive feature reduction with varied missing data and feature selection for arthritis disease prediction

In the expansive domain of data-driven research, the curse of dimensionality poses challenges such as increased computational complexity, noise sensitivity, and the risk of overfitting models. Dimensionality reduction is vital to handle high-dimensional datasets effectively. The pilot study disease dataset (PSD) with 53 features contains patients with Rheumatoid Arthritis (RA) and Osteoarthritis (OA). Our work aims to reduce the dimension of the features in the PSD dataset, identify a suitable feature selection technique for the reduced-dimensional dataset, analyze an appropriate Machine Learning (ML) model, select significant features to predict the RA and OA disease and reveal significant features that predict the arthritis disease. The proposed study, Progressive Feature Reduction with Varied Missing Data (PFRVMD), was employed to reduce the dimension of features by using PCA loading scores in the random value imputed PSD dataset. Subsequently, notable feature selection methods, such as backward feature selection, the Boruta algorithm, the extra tree classifier, and forward feature selection, were implemented on the reduced-dimensional feature set. The significant features/biomarkers are obtained from the best feature selection technique. ML models such as the K-Nearest Neighbour Classifier (KNNC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Naïve Bayes Classifier (NBC), Random Forest Classifier (RFC) and Support Vector Classifier (SVC) are used to determine the best feature selection method. The results indicated that the Extra Tree Classifier (ETC) is the promising feature selection method for the PSD dataset because the significant features obtained from ETC depicted the highest accuracy on SVC.

Effective and Efficient Android Malware Detection and Category Classification Using the Enhanced KronoDroid Dataset

Android is the most widely used mobile operating system and responsible for handling a wide variety of data from simple messages to sensitive banking details. The explosive increase in malware targeting this platform has made it imperative to adopt machine learning approaches for effective malware detection and classification. Since its release in 2008, the Android platform has changed substantially and there has also been a significant increase in the number, complexity, and evolution of malware that target this platform. This rapid evolution quickly renders existing malware datasets out of date and has a degrading impact on machine learning-based detection models. Many studies have been carried out to explore the effectiveness of various machine learning models for Android malware detection. Majority of these studies use datasets that have compiled using static or dynamic analysis of malware but the use of hybrid analysis approaches has not been addressed completely. Likewise, the impact of malware evolution has not been fully investigated. Although some of the models have achieved exceptional results, their performance deteriorated for evolving malware and they were also not effective against antidynamic malware. In this paper, we address both these limitations by creating an enhanced subset of the KronoDroid dataset and using it to develop a supervised machine learning model capable of detecting evolving and antidynamic malware. The original KronoDroid dataset contains malware samples from 2008 to 2020, making it effective for the detection of evolving malware and handling concept drift. Also, the dynamic features are collected by executing the malware on a real device, making it effective for handling antidynamic malware. We create an enhanced subset of this dataset by adding malware category labels with the help of multiple online repositories. Then, we train multiple supervised machine learning models and use the ExtraTree classifier to select the top 50 features. Our results show that the random forest (RF) model has the highest accuracy of 98.03% for malware detection and 87.56% for malware category classification (for 15 malware categories).