Machine Learning Classification Algorithms Research Articles

Comparison of classical Machine Learning-based algorithms to predict Triplex Forming Oligonucleotides

Machine learning algorithms can be utilized to tackle genomic challenges in biological systems, such as identifying therapeutic targets for drug development and searching for effective treatments for chronic diseases like cancer. Triplex-forming oligonucleotides (TFOs) can recognize specific sites in the major groove of duplex DNA sequences, aiding in the identification of therapeutic targets via computational methods. This study aims to compare the performance of five classical machine learning algorithms—Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—in classifying single-stranded DNA into TFOs. Three datasets were created from an oligo library targeting the supF gene, developed by Kaufmann et al., and used to train the algorithms. The classifiers were optimized through hyperparameter tuning and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. SVM and kNN achieved less than 90 % accuracy, while DT, RF, and XGBoost, which used tree-based methods, attained over 90 % accuracy. The XGBoost model showed the best performance with over 96 % accuracy in classifying single-stranded DNA into TFOs. This study demonstrates that supervised machine learning techniques are effective for accurately classifying single-stranded DNA into TFOs.

Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended.

Classification of Latilactobacillus sakei subspecies based on MALDI-TOF MS protein profiles using machine learning models.

Latilactobacillus sakei plays a significant role in the realm of food bacteria. One particular subspecies of L. sakei is employed as a protective agent during food fermentation, whereas another strain is responsible for food spoilage. Hence, it is crucial to precisely differentiate between the two subspecies of L. sakei. In this study, machine learning models based on protein mass peaks were developed for the first time to distinguish L. sakei subspecies. Furthermore, the efficacy of three commonly used machine learning algorithms for microbial classification was evaluated. Our results provide the foundation for future research on developing machine learning models for the classification of microbial species or subspecies. In addition, the developed model can be used in the food industry to monitor L. sakei subspecies in fermented foods in a time- and cost-effective method for food quality and safety.

A Review on Utilizing Machine Learning Classification Algorithms for Skin Cancer

Skin cancer is one of the most prevalent forms of cancer globally, with rising incidence rates posing significant challenges to healthcare systems. Early detection and accurate diagnosis are critical for effective treatment and patient outcomes. In recent years, machine learning (ML) algorithms have emerged as powerful tools for analyzing medical imaging data and assisting clinicians in diagnosing skin cancer. This review paper provides a comprehensive overview of the ML classification algorithms in the context of skin cancer detection and diagnosis. We discuss various types of skin cancer, including melanoma, basal cell carcinoma, and squamous cell carcinoma, along with their characteristics and diagnostic challenges. Furthermore, we review the current state-of-the-art ML techniques, such as support vector machines (SVM), K-Nearest Neighbor (KNN), and convolutional neural network (CNN), highlighting their strengths and limitations in skin cancer classification. A systematic search of academic databases, including Scopus, ResearchGate, Google Scholar, IEEE Xplore, Wiley Online Library, Elsevier, ScienceDirect, and Springer, was conducted. Continued evolution in skin cancer classification promises enhanced diagnostic accuracy and personalized treatment strategies.

Comparing PCA-Based Machine Learning Algorithms for COVID-19 Classification Using Chest X-ray Images

أدى الانتشار السريع لجائحة كوفيد-19 إلى إجهاد أنظمة الرعاية الصحية العالمية، مما استلزم أساليب تشخيص فعالة. في حين أن تفاعل البوليميراز المتسلسل (PCR) واختبارات المستضدات شائعة، إلا أن لها حدودًا في السرعة والدقة. يعد تعزيز دقة تقنيات التصوير، وخاصة )الأشعة السينية للصدر( و)التصوير المقطعي المحوسب(، أمرًا بالغ الأهمية للكشف عن تشوهات الرئة المرتبطة بكوفيد-19. يُفضل استخدام الأشعة السينية للصدر، لكونه فعال من حيث التكلفة ويمكن الوصول إليه، على الأشعة المقطعية، لكن التشخيص الدقيق غالبًا ما يتطلب دعمًا تكنولوجيًا. ولمعالجة هذه المشكلة، تتوفر مجموعة بيانات شاملة لصور الأشعة السينية للصدر مصنفة إلى خمس فئات على Kaggle . تتضمن معالجة مثل هذه البيانات خطوات مثل تحويل (التدرج الرمادي، وضبط كثافة الصورة، وتغيير الحجم، واستخراج الميزات باستخدام تحليل المكونات الرئيسية). تقنيات التعلم الآلي، بما في ذلك (شجرة القرار)، و(الغابات العشوائية)، و(الانحدار التدرجي العشوائي)، و(الانحدار اللوجستي)، و( نظرية البايزي الساذج)، و (خوارزمية الجيران الاقرب)، هي تقنيات المستخدمة لتصنيف الصور. يُظهر (شجرة القرار) أعلى دقة بنسبة 88⸓، متفوقًا على النماذج الأخرى مثل ( نظرية البايزي الساذج 77⸓ )، و (خوارزمية الجيران الاقرب 71⸓ (، (الانحدار التدرجي العشوائي 70⸓ )، (الانحدار اللوجستي 74⸓)، الغابات العشوائية 45⸓ شجرة القرار إنه يتفوق باستمرار عبر مقاييس التقييم مثل درجة الضبط، والحساسية، الدقة، والاستدعاء، بمتوسط مرجح بنسبة 88⸓ . ومع ذلك، يعتمد اختيار النموذج الأمثل لتعلم الآلة على عوامل مثل خصائص مجموعة البيانات وتفاصيل التنفيذ. وبالتالي، يعد النظر بعناية في هذه العوامل أمرًا بالغ الأهمية عند اختيار نموذج تعلم الآلة لتشخيص كوفيد-19 عبر تصنيف صور الأشعة السينية للصدر.

Supervised Machine Learning Algorithm for Stellar Classification

Supervised Machine Learning Algorithm for Stellar Classification

Assessment of few-hits machine learning classification algorithms for low-energy physics in liquid argon detectors

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors (“Module of Opportunity”).

Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering

PurposeThis study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).Design/methodology/approachThe main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).FindingsThe greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.Practical implicationsThis research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.Originality/valueAlthough turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

Factors affecting biochemical pregnancy loss (BPL) in preimplantation genetic testing for aneuploidy (PGT-A) cycles: machine learning-assisted identification

PurposeTo determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.MethodsThe study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models.ResultsThroughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age.ConclusionsThe Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.

Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms

The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the “SensorNetGuard” dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The “SensorNetGuard” dataset is available on the IEEE data port and Kaggle platform.

Optimizing Curriculum Vitae Concordance: A Comparative Examination of Classical Machine Learning Algorithms and Large Language Model Architectures

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinized the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs such as GPT-4, GPT-3, and LLAMA adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluated these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain their accuracy, efficiency, and scalability. Our results revealed that while conventional models excel at processing structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.

POLICIAMENTO PREDITIVO E INTELIGÊNCIA ARTIFICIAL: ANÁLISE DE DESEMPENHO DO ALGORITMO DE APRENDIZADO DE MÁQUINA SUPERVISIONADO RANDOM FOREST NA PREDIÇÃO DE OCORRÊNCIAS POLICIAIS DE ROUBO NAS ZONAS DA REGIÃO METROPOLITANA DE SÃO LUÍS

Predictive policing is a concept based on the premise that it is possible to predict when and where crimes will occur again in the future by using sophisticated computer analysis based on data from previously committed crimes (UCHIDA, as cited in NORTON, 2013, pp. 32–33). The aim of this article is to analyze the spatial prediction of crimes, highlighting the importance of innovative approaches in the field of public safety management. Specifically, the article explores practical applications of using Artificial Intelligence (AI) through supervised machine learning algorithms for classification, using the emergency call records database (190) of robbery incidents in the metropolitan region of São Luís as input, and comparing results through validation techniques for the Random Forest algorithm. Through detailed analysis of these data, the potential of predictive policing is illustrated not only to anticipate criminal events but also to develop a computational tool capable of assisting and supporting strategic decision-making, significantly contributing to troop allocation management and, consequently, to the enhancement of preventive urban patrol services.

Autism detection based on autism spectrum quotient using weighted average ensemble method

Autism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing values and feature selection occurs, where the feature selection method being utilized is p-value. The model in this research uses the weighted average ensemble method, which is the model that combines three machine learning classification algorithms. Eight classification algorithms are tested to identify the three algorithms with the best performance, namely gaussian Naïve Bayes (NB), logistic regression (LR), and random forest (RF). Following the testing, the model constructed using the weighted average ensemble method exhibits the highest performance compared to the model built using a single classification algorithm. The performance matrix used to measure the model’s performance is area under the curve (AUC)/receiver operating characteristic (ROC), with the developed model achieving an AUC/ROC value of 0.912.

A novel approach for assessing fairness in deployed machine learning algorithms

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, from healthcare decisions to legal judgments. Many studies show evidence of unfair ML outcomes. However, the current body of literature lacks a statistically validated approach that can evaluate the fairness of a deployed ML algorithm against a dataset. A novel evaluation approach is introduced in this research based on k-fold cross-validation and statistical t-tests to assess the fairness of ML algorithms. This approach was exercised across five benchmark datasets using six classical ML algorithms. Considering four fair ML definitions guided by the current literature, our analysis showed that the same dataset generates a fair outcome for one ML algorithm but an unfair result for another. Such an observation reveals complex, context-dependent fairness issues in ML, complicated further by the varied operational mechanisms of the underlying ML models. Our proposed approach enables researchers to check whether deploying any ML algorithms against a protected attribute within datasets is fair. We also discuss the broader implications of the proposed approach, highlighting a notable variability in its fairness outcomes. Our discussion underscores the need for adaptable fairness definitions and the exploration of methods to enhance the fairness of ensemble approaches, aiming to advance fair ML practices and ensure equitable AI deployment across societal sectors.

Machine Learning Algorithms for Sex Classification by Using Variables of Orbital Structures: A Computed Tomography Study

Machine Learning Algorithms for Sex Classification by Using Variables of Orbital Structures: A Computed Tomography Study

Comparative analysis of heart failure prediction using machine learning models

Heart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the likelihood of heart failure occurrence in individuals. In our research, the heart failure prediction and comparison are done using Logistic Regression, KNN, SVM, decision tree and random forest The accurate identification of high-risk individuals enables early intervention and better management of heart failure, reducing the risk of mortality and morbidity associated with this condition. Overall, machine learning algorithms play a major role in improving the accuracy of heart failure risk assessment, allowing for more personalized and effective prevention and treatment strategies.

Music Emotion Classification: A Literature Review

Music, as an art form, combines rhythm and sound to form a functional melodic structure, uniquely capable of conveying emotions non-verbally. Within the domain of Music Information Retrieval (MIR), Music Emotion Classification (MEC) represents a specialized subset dedicated to the identification and labeling of emotional attributes in songs. This is achieved by extracting and comparing features from musical compositions. This research aims to discern the contemporary landscape of research and its associated research gaps in this domain. The study comprised a collection of publications found through searches conducted on Google Scholar between the years 2006 and 2023, with the search terms: Music Emotion Classification, Music Emotion Classification in Sri Lanka, Music Emotion Recognition, and Emotion Classification in Music. This study was narrowed down to the research that utilized audio files for classification. Among the initial set of 42 studies, 20 were selected for detailed analysis using the purposive sampling method. The review encompassed essential aspects, including acoustic feature analysis, emotional modeling, classification methodologies, and performance evaluation. The findings highlighted a paucity of research considering cultural, regional, and linguistic variations. The most often used acoustic features encompassed rhythm, pitch, timbre, spectral, and harmony whereas the most frequently used emotion categories for the classification were happiness, anger, sadness, and relaxation. Support Vector Machine (SVM) was the most used machine learning algorithm for classification, although regression methods, neural network-based approaches, and fuzzy classifications were also explored. Notably, the adoption of multi-modal approaches for emotion classification, as well as multi-labeled emotion classification, remained limited. These insights underscore the need for future research to address the cultural and language diversity of datasets, explore innovative classification techniques, and embrace multi-modal and multi-labeled emotional classification methodologies in the context of music emotion classification within MIR.

IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD

Ovarian cancer can be identified from microarray data using machine learning. Many studies only focus on improving the machine learning classification algorithms to achieve higher performance. The purpose of classification is not only to obtain high performance but also to seek new knowledge from the results. This research focuses on both. By using a hybrid Supervised Infinite Feature Selection (SIFS) method with Classification and Regression Tree (CART) or SIFS-CART, this research aims to predict ovarian cancer and identify potential genes for ovarian cancer cases. The data used is the OVA_ovary dataset. SIFS in the best SIFS-CART model reduced 10935 genes in the initial OVA_ovary dataset to 1000 genes. Then, CART was built with these 1000 genes. Based on the balanced accuracy (BA) metric for imbalanced microarray data, the best SIFS-CART model achieves 85.7% BA in training and 83.2% in testing. The optimal CART in the best SIFS-CART model only needs four genes from 1000 selected genes to build it. Those genes are STAR, WT1, PEG3, and ASPN. Based on studies of several pieces of literature in the medical field, it can be concluded that STAR, WT1, and PEG3 play an important role in ovarian cancer cases. However, the relationship between ASPN and ovarian cancer in more detail has not been studied by medical researchers.

A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.