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Journal articles on the topic 'Supervised Machine Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Sabeti, Behnam, Hossein Abedi Firouzjaee, Reza Fahmi, Saeid Safavi, Wenwu Wang, and Mark D. Plumbley. "Credit Risk Rating Using State Machines and Machine Learning." International Journal of Trade, Economics and Finance 11, no. 6 (2020): 163–68. http://dx.doi.org/10.18178/ijtef.2020.11.6.683.

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Credit risk is the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations. With the growing number of customers and expansion of businesses, it’s not possible or at least feasible for banks to assess each customer individually in order to minimize this risk. Machine learning can leverage available user data to model a behavior and automatically estimate a credit score for each customer. In this research, we propose a novel approach based on state machines to model this problem into a classical supervised machine learning task. The proposed sta

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Bzdok, Danilo, Martin Krzywinski, and Naomi Altman. "Machine learning: supervised methods." Nature Methods 15, no. 1 (2018): 5–6. http://dx.doi.org/10.1038/nmeth.4551.

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Jha, Ritambhara. "Transforming Manufacturing Sector with Supervised Machine Learning Techniques." International Journal of Science and Research (IJSR) 10, no. 4 (2021): 1367–69. http://dx.doi.org/10.21275/sr24203180211.

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Verma, Shivangi, and Chhavi Chaudhary. "Supervised Machine Learning: A Review on Regression Technique." International Journal of Research Publication and Reviews 6, sp5 (2025): 79–87. https://doi.org/10.55248/gengpi.6.sp525.1911.

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d

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Amrita, Sadarangani *. Dr. Anjali Jivani. "A SURVEY OF SEMI-SUPERVISED LEARNING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 138–43. https://doi.org/10.5281/zenodo.159333.

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Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classification, and unsupervised clustering and semi-supervised clustering are also discussed.

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Sharma, Swapnil. "Supervised Learning: An InDepth Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35414.

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Supervised learning pivotal machine learning paradigm wherein models are trained on labeled datasets. They predict outcomes or classify data. It includes methodologies and diverse applications of supervised learning. Emphasizing significance in modern technology. Key methodologies encompass linear regression logistic regression. Also decision trees, support vector machines neural networks. Each with unique advantages for specific tasks. Versatility is demonstrated through applications in image and speech recognition. Natural language processing, medical diagnosis and financial forecasting also

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Ma, Jun, Yakun Wen, and Liming Yang. "Lagrangian supervised and semi-supervised extreme learning machine." Applied Intelligence 49, no. 2 (2018): 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.

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Asharef, Dhahabiyya. "COVID-19 Future Forecasting Using Supervised Machine Learning Models." International Journal of Science and Research (IJSR) 11, no. 2 (2022): 960–67. http://dx.doi.org/10.21275/sr22221205524.

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Dr, A. R. JayaSudha, and Anes J. Mohammed. "Malware Analysis Using Supervised Machine Learning." Recent Innovations in Wireless Network Security 5, no. 1 (2023): 24–32. https://doi.org/10.5281/zenodo.7797951.

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<em>A research endeavor in the field of cyber security is being carried out under the working title of "Malware Analysis Using Supervised Machine Learning." For the purpose of identifying malicious software in the system, this initiative makes use of supervised machine learning. This endeavor relied heavily on using primary sources for its material. There is also something called dynamic malware analysis, which is when the software is analyzed in the malware analysis facility after it has been run. The environment is implemented on a Flare VM running the Windows 10 distribution. In o

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Biswas, Aditya, Ishan Saran, and F. Perry Wilson. "Introduction to Supervised Machine Learning." Kidney360 2, no. 5 (2021): 878–80. http://dx.doi.org/10.34067/kid.0000182021.

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Fares, Ahmed H., Mohamed I. Sharawy, and Hala H. Zayed. "Intrusion Detection: Supervised Machine Learning." Journal of Computing Science and Engineering 5, no. 4 (2011): 305–13. http://dx.doi.org/10.5626/jcse.2011.5.4.305.

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13

Iosifidis, Alexandros. "Extreme learning machine based supervised subspace learning." Neurocomputing 167 (November 2015): 158–64. http://dx.doi.org/10.1016/j.neucom.2015.04.083.

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Butlin, Patrick. "Machine Learning, Functions and Goals." Croatian journal of philosophy 22, no. 66 (2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.

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Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to

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15

Gupta, Monica. "A Comparative Study on Supervised Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1023–28. http://dx.doi.org/10.22214/ijraset.2022.39980.

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Abstract: Machine learning enables computers to act and make data driven decisions rather than being explicitly programmed to carry out a certain task. It is a tool and technology which can answer the question from your data. These programs are designed to learn and improve over time when exposed to new data. ML is a subset or a current application of AI. It is based on an idea that we should be able to give machines access to data and let them learn from themselves. ML deals with extraction of patterns from dataset, this means that machines can not only find the rules for optimal behavior but

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Ma, Jun, Yakun Wen, and Liming Yang. "Fisher-regularized supervised and semi-supervised extreme learning machine." Knowledge and Information Systems 62, no. 10 (2020): 3995–4027. http://dx.doi.org/10.1007/s10115-020-01484-x.

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Anist., A., Raja. M. Ganapathy, M. Charles., and V. Vignesh. "Machine Failure Prediction using Supervised Machine Learning Technique." International Journal of Multidisciplinary Research Transactions 5, no. 6 (2023): 325–37. https://doi.org/10.5281/zenodo.7908959.

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We focus on machine failure prediction in industry 4.0.Indeed, it is used for classification problems on the reliability and quality of their machines and products. We compare machine learning methods applied to a difficult real-world problem: predicting machine failure using attributes monitored internally by individual parts. The problem is one of detecting rare events in a time series of noisy and non-parametrically-distributed data. We develop a new algorithm based on the multiple-instance learning framework and the Regression algorithm which is specifically designed for the classification

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Jha, Ritambhara. "Classifying High Risk Diabetic Patients using Supervised Machine Learning Models." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 806–11. http://dx.doi.org/10.21275/sr231209033422.

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Singh, Mr Harendra Pratap, and Dr Manoj Kumar Pandey. "Analysis of Supervised Machine Learning Techniques to Assess Water Quality." International Journal of Research Publication and Reviews 6, sp5 (2025): 23–29. https://doi.org/10.55248/gengpi.6.sp525.1903.

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Nkemdilim, Mbeledogu Njideka, Paul Roseline Uzoamaka, Ugoh Daniel, and Mbeledogu Kaodilichukwu Chidi. "An Overview of Supervised Machine Learning Paradigms and their Classifiers." International Journal of Advanced Engineering, Management and Science 10, no. 3 (2024): 24–32. http://dx.doi.org/10.22161/ijaems.103.4.

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Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the

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Ezadeen Mehyadin, Aska, and Adnan Mohsin Abdulazeez. "CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW." Iraqi Journal for Computers and Informatics 47, no. 1 (2021): 1–11. http://dx.doi.org/10.25195/ijci.v47i1.277.

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Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serv

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J., Dr SIRISHA. "Assessing DDoS Detection Accuracy through Semi-Supervised Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29861.

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Despite the proliferation of advanced Machine Learning (ML) techniques in DDoS detection, this pervasive attack remains a significant menace to the Internet's integrity. Existing ML based DDoS detection methods fall into two categories: supervised and unsupervised approaches. This paper synthesizes insights from existing research endeavors, and enhance DDoS detection through machine learning methodologies, specifically focusing on semi-supervised techniques for analysis purposes. By harnessing the power of semi-supervised ML, we employ a succession of algorithms including Naive Bayes, Support

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23

Jaroszewski, Daniel, Benedikt Sturm, Wolfgang Mergenthaler, et al. "Supervised Learning Using Quantum Technology." PHM Society European Conference 5, no. 1 (2020): 7. http://dx.doi.org/10.36001/phme.2020.v5i1.1275.

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In this paper, we present classical machine learning algorithms enhanced by quantum technology to classify a data set. The data set contains binary input variables and binary output variables. The goal is to extend classical approaches such as neural networks by using quantum machine learning principles. Classical algorithms struggle as the dimensionality of the feature space increases. We examine the usage of quantum technologies to speed up these classical algorithms and to introduce the new quantum paradigm into machine diagnostic domain. Most of the prognosis models based on binary or mult

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Rass, Stefan, Sandra König, Jasmin Wachter, Manuel Egger, and Manuel Hobisch. "Supervised Machine Learning with Plausible Deniability." Computers & Security 112 (January 2022): 102506. http://dx.doi.org/10.1016/j.cose.2021.102506.

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Muhammad, Iqbal, and Zhu Yan. "SUPERVISED MACHINE LEARNING APPROACHES: A SURVEY." ICTACT Journal on Soft Computing 05, no. 03 (2015): 946–52. http://dx.doi.org/10.21917/ijsc.2015.0133.

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Bhardwaj, Priyank. "Analysis of Supervised Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 2309–12. http://dx.doi.org/10.22214/ijraset.2020.5377.

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Kabou, Salheddine, Zinelaabidine Rabhi, Abdeallah Hadj Seddik, and Ramadhan Masmoudi. "Data anonymization through supervised Machine Learning." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 3 (2024): e12696. https://doi.org/10.54021/seesv5n3-059.

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In the digital era, protecting personal data has become increasingly important as organizations collect vast amounts of sensitive information. Data anonymization techniques, such as k-anonymity, aim to strike a balance between privacy preservation and data utility. In this study, we applied supervised machine learning techniques to assess the impact of anonymization on data utility and privacy. Specifically, we evaluated k-anonymity and l-diversity models using four supervised learning methods: Naive Bayes, K-nearest neighbors, Decision Trees, and Random Forest. By measuring classification acc

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28

Pei, Huimin, Kuaini Wang, Qiang Lin, and Ping Zhong. "Robust semi-supervised extreme learning machine." Knowledge-Based Systems 159 (November 2018): 203–20. http://dx.doi.org/10.1016/j.knosys.2018.06.029.

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29

Krishnasamy, Ganesh, and Raveendran Paramesran. "Hessian semi-supervised extreme learning machine." Neurocomputing 207 (September 2016): 560–67. http://dx.doi.org/10.1016/j.neucom.2016.05.039.

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Nguyen Thi Thu, Thuy, and Vuong Dang Xuan. "FoRex Trading Using Supervised Machine Learning." International Journal of Engineering & Technology 7, no. 4.15 (2018): 400. http://dx.doi.org/10.14419/ijet.v7i4.15.23024.

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The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM)

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Febrian, Muhammad Exell, Fransiskus Xaverius Ferdinan, Gustian Paul Sendani, Kristien Margi Suryanigrum, and Rezki Yunanda. "Diabetes prediction using supervised machine learning." Procedia Computer Science 216 (2023): 21–30. http://dx.doi.org/10.1016/j.procs.2022.12.107.

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Jiang, Tammy, Jaimie L. Gradus, and Anthony J. Rosellini. "Supervised Machine Learning: A Brief Primer." Behavior Therapy 51, no. 5 (2020): 675–87. http://dx.doi.org/10.1016/j.beth.2020.05.002.

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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimen

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Sineglazov, Victor, Olena Chumachenko, and Eduard Heilyk. "Semi-controlled Learning in Information Processing Problems." Electronics and Control Systems 4, no. 70 (2022): 37–43. http://dx.doi.org/10.18372/1990-5548.70.16754.

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The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi

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R., KARTHIKEYAN, and SELVANANDHINI B. "A SYSTEMATIC REVIEW OF PREDICTION OF HEART DISEASES USING SUPERVISED MACHINE LEARNING ALGORITHMS." IJRSET SEPTEMBER Volume 9 Issue 9 9, no. 9 (2022): 1–9. https://doi.org/10.5281/zenodo.7049471.

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Machine learning algorithms are playing a major role in prediction of heart diseases. As compared to other diseases, heart disease is getting more attention today since it affects humans unexpectedly. Early detection of heart diseases will save human lives. Many researchers have published numerous articles in prediction analysis of heart disease with the support of machine learning algorithms. Especially, supervised learning algorithms are getting more attention with respect to heart diseases prediction. This paper provides a system review of heart diseases prediction with the help of sup

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Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure

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Siddika, Ayesha, Momotaz Begum, Fahmid Al Farid, Jia Uddin, and Hezerul Abdul Karim. "Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms." Eng 6, no. 7 (2025): 161. https://doi.org/10.3390/eng6070161.

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In today’s fast-paced world of software development, it is essential to ensure that programs run smoothly without any issues. When dealing with complex applications, the objective is to predict and resolve problems before they escalate. The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervis

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Wang, Yanyan, Qun Chen, Murtadha H. M. Ahmed, et al. "Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis." Transactions of the Association for Computational Linguistics 11 (2023): 723–39. http://dx.doi.org/10.1162/tacl_a_00571.

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Abstract Recent work has shown that Aspect-Term Sentiment Analysis (ATSA) can be effectively performed by Gradual Machine Learning (GML). However, the performance of the current unsupervised solution is limited by inaccurate and insufficient knowledge conveyance. In this paper, we propose a supervised GML approach for ATSA, which can effectively exploit labeled training data to improve knowledge conveyance. It leverages binary polarity relations between instances, which can be either similar or opposite, to enable supervised knowledge conveyance. Besides the explicit polarity relations indicat

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val

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Gaurav, Kashyap. "Self-Supervised Learning: How Self-Supervised Learning Approaches Can Reduce Dependence on Labeled Data." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 4 (2024): 1–10. https://doi.org/10.5281/zenodo.14507625.

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A promising paradigm that lessens the need for sizable labeled datasets for machine learning model training is self-supervised learning (SSL). SSL models are able to learn data representations through pretext tasks by utilizing unlabeled data. These representations can then be refined for tasks that come after. The development of self-supervised learning, its underlying techniques, and its potential to address the difficulties associated with obtaining labeled data are all examined in this paper. We go over the main self-supervised methods, their uses, and how they might improve the generaliza

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Awasthi, Shivani. "Dropout Prediction with Supervised Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44873.

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Student dropout is a critical issue in the education sector, impacting institutional efficiency and student success. This project, Dropout Prediction with Supervised Learning, leverages machine learning models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), and Naïve Bayes (NB)—to predict student dropouts based on historical academic, demographic, and behavioural data. The study involves data preprocessing, feature selection, and model evaluation to identify key factors influencing dropout rates. Supervised learning te

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Hardanto, Lilik T., and Lili Ayu Wulandhari. "Lithofacies Classification Using Supervised and Semi-Supervised Machine Learning Approach." International Journal on Advanced Science, Engineering and Information Technology 11, no. 2 (2021): 542. http://dx.doi.org/10.18517/ijaseit.11.2.11764.

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Kühl, Niklas, Robin Hirt, Lucas Baier, Björn Schmitz, and Gerhard Satzger. "How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card." Communications of the Association for Information Systems 48, no. 1 (2021): 589–615. http://dx.doi.org/10.17705/1cais.04845.

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Gu, Qiang, Anup Kumar, Simon Bray, et al. "Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine." PLOS Computational Biology 17, no. 6 (2021): e1009014. http://dx.doi.org/10.1371/journal.pcbi.1009014.

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Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scientists by enabling them to perform end-to-end reproducible machine learning analyses at large scale using only a web browser. Galaxy-ML extends Galaxy (https://galaxyproject.org), a biomedical computational workbench used by tens of thousands of scientists across the world, with a suite of tools for all aspects of supervised machine learning.

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Adrin, Muchatibaya, and David Fadaralika. "A Comparative Model for Predicting Customer Churn using Supervised Machine Learning." International Journal of Science and Research (IJSR) 11, no. 2 (2022): 133–36. http://dx.doi.org/10.21275/sr22131110718.

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Narayan Koranchirath, Nithin. "Deciphering the Dynamics of Hospital Readmissions Patterns Using Supervised Machine Learning." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 715–25. http://dx.doi.org/10.21275/sr24408093905.

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Pandey, Prof Divya, Prof Zeba Vishwakarma, Shubhangi Soni, and Akanksha Mishra. "Efficient Machine Learning Through Self-Supervised Learning: Methodologies and Applications." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 03 (2023): 1–14. http://dx.doi.org/10.15680/ijirset.2023.1203141.

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The field of machine learning has recently undergone significant advancements, driven by the development of sophisticated algorithms and the availability of large datasets. Among the various paradigms of machine learning, supervised learning has traditionally dominated, relying heavily on labeled data to train models. However, acquiring and annotating large amounts of labeled data is both time-consuming and costly, leading to a growing interest in self-supervised learning (SSL). This promising approach leverages unlabeled data to learn useful representations, which can then be fine-tuned for s

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Murali, Nikitha, Ahmet Kucukkaya, Alexandra Petukhova, John Onofrey, and Julius Chapiro. "Supervised Machine Learning in Oncology: A Clinician's Guide." Digestive Disease Interventions 04, no. 01 (2020): 073–81. http://dx.doi.org/10.1055/s-0040-1705097.

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AbstractThe widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, s

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Ma, Jun, and Guolin Yu. "Lagrangian Regularized Twin Extreme Learning Machine for Supervised and Semi-Supervised Classification." Symmetry 14, no. 6 (2022): 1186. http://dx.doi.org/10.3390/sym14061186.

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Twin extreme learning machine (TELM) is a phenomenon of symmetry that improves the performance of the traditional extreme learning machine classification algorithm (ELM). Although TELM has been widely researched and applied in the field of machine learning, the need to solve two quadratic programming problems (QPPs) for TELM has greatly limited its development. In this paper, we propose a novel TELM framework called Lagrangian regularized twin extreme learning machine (LRTELM). One significant advantage of our LRTELM over TELM is that the structural risk minimization principle is implemented b

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Omankwu Obinnaya Chinecherem, Ugwuja Nnenna Esther, and Kanu Chigbundu. "Comprehensive review of supervised machine learning algorithms to identify the best and error free." International Journal of Scholarly Research in Engineering and Technology 2, no. 1 (2023): 013–19. http://dx.doi.org/10.56781/ijsret.2023.2.1.0028.

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Abstract:

Supervised classification is one of the tasks most frequently carried out by the intelligent systems. Supervised Machine Learning (SML) is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. This paper; compares various supervised. Seven different machine learning algorithms were considered: Decision Table, Random Forest (RF) , Naïve Bayes (NB) , Support Vector Machine (SVM), Neural Networks (Perceptron), JRip and Decision Tree (J48)l. And also reviews various Supervised Machine Learning (ML

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