Relevant bibliographies by topics / Multiclass Evaluation Metrics

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Multiclass Evaluation Metrics'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Multiclass Evaluation Metrics"

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Nabi, Rebwar M. "Multiclass Classifier for Stock Price Prediction." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 4157–69. http://dx.doi.org/10.17762/turcomat.v12i3.1707.

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The stock market has been a crucial factor of investments in the financial domain. Risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock Price forecasting is complex that could have a significant influence on the financial market. The Machine Learning (ML) type of artificial intelligence (AI) provides a more accurate forecast for binary and multiclass classification. Different effective methods have been recommended to resolve the problem in the binary classification case but the multiclass classification case is a more delica
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Zahra, F., NZ Jhanjhi, Sarfraz Nawaz Brohi, Navid Ali Khan, Mehedi Masud, and Mohammed A. AlZain. "Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning." Sensors 22, no. 18 (2022): 6765. http://dx.doi.org/10.3390/s22186765.

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The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly
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Rácz, Bajusz, and Héberger. "Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics." Molecules 24, no. 15 (2019): 2811. http://dx.doi.org/10.3390/molecules24152811.

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Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-e
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V, Dr Savitha, Dr Karthick M, and Dr T. Kalaikumaran. "Parasitic Egg detection from Microscopic images using Convolutional Neural Networks." Tamjeed Journal of Healthcare Engineering and Science Technology 1, no. 1 (2023): 24–34. http://dx.doi.org/10.59785/tjhest.v1i1.3.

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The most common test for parasitic infection diagnosis is stool parasitological testing. The use of the Kato-Katz method in the preparation of slides for the development of the image bank discussed here was extremely important. Other authors' studies on the same topic were discussed. Various parasite eggs of various species were created. Then the binary and multiclass classifier architectures were empirically defined, and each model was implemented. The performance of the classifiers was evaluated using metrics recommended in the literature for both empirically defined and transfer learning ar
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Bhowmick, Karan, and Vivek Sarvaiya. "A COMPARATIVE STUDY OF THE DIFFERENT CLASSIFICATION ALGORITHMS ON FOOTBALL ANALYTICS." International Journal of Advanced Research 9, no. 08 (2021): 392–407. http://dx.doi.org/10.21474/ijar01/13280.

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Sports analytics is on the rise, with many teams looking to use data science and machine learning algorithms to augment their teams research and boost team performance. This is especially true in the case of Football Clubs. In this work, we have taken the statistics of matches for each team from five major football leagues. These include the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. We use this data for two kinds of classification to predict a teams win, loss, or draw. First, we implement Multiclass Classification using Naive Bayes classification, Decision Tree classif
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Manzano, C., C. Meneses, P. Leger, and H. Fukuda. "An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection." Complexity 2022 (April 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/6760920.

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Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, an
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Hadinata, Patrick Nicholas, Djoni Simanta, Liyanto Eddy, and Kohei Nagai. "Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+." Applied Sciences 13, no. 4 (2023): 2398. http://dx.doi.org/10.3390/app13042398.

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Monitoring damage in concrete structures is crucial for maintaining the health of structural systems. The implementation of computer vision has been the key for providing accurate and quantitative monitoring. Recent development uses the robustness of deep-learning-aided computer vision, especially the convolutional neural network model. The convolutional neural network is not only accurate but also flexible in various scenarios. The convolutional neural network has been constructed to classify image in terms of individual pixel, namely pixel-level detection, which is especially useful in detec
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Danishuddin, Vikas Kumar, Shraddha Parate, et al. "Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer." Pharmaceuticals 14, no. 7 (2021): 699. http://dx.doi.org/10.3390/ph14070699.

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Disruption of epigenetic processes to eradicate tumor cells is among the most promising interventions for cancer control. EZH2 (Enhancer of zeste homolog 2), a catalytic component of polycomb repressive complex 2 (PRC2), methylates lysine 27 of histone H3 to promote transcriptional silencing and is an important drug target for controlling cancer via epigenetic processes. In the present study, we have developed various predictive models for modeling the inhibitory activity of EZH2. Binary and multiclass models were built using SVM, random forest and XGBoost methods. Rigorous validation approach
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Donisi, Leandro, Carlo Ricciardi, Giuseppe Cesarelli, et al. "Bidimensional and Tridimensional Poincaré Maps in Cardiology: A Multiclass Machine Learning Study." Electronics 11, no. 3 (2022): 448. http://dx.doi.org/10.3390/electronics11030448.

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Heart rate is a nonstationary signal and its variation may contain indicators of current disease or warnings about impending cardiac diseases. Hence, heart rate variation analysis has become a noninvasive tool to further study the activities of the autonomic nervous system. In this scenario, the Poincaré plot analysis has proven to be a valuable tool to support cardiac diseases diagnosis. The study’s aim is a preliminary exploration of the feasibility of machine learning to classify subjects belonging to five cardiac states (healthy, hypertension, myocardial infarction, congestive heart failur
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Singh, Sagar, Ilya Tsvankin, and Ehsan Zabihi Naeini. "Facies prediction with Bayesian inference: Application of supervised and semisupervised deep learning." Interpretation 10, no. 2 (2022): T279—T290. http://dx.doi.org/10.1190/int-2021-0104.1.

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Accurate delineation of geologic facies and determination of live fluids from seismic reflection data is crucial for reservoir characterization during petroleum exploration. Facies classification or fluid identification is often done manually by an experienced interpreter, which makes this process subjective, laborious, and time-consuming. Several machine-learning models have been proposed to automate multiclass facies segmentation, but significant practical challenges (e.g., limited scope of labels for training purposes, skewed data distribution, inefficient performance evaluation metrics, et
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Dissertations / Theses on the topic "Multiclass Evaluation Metrics"

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Harish, Guruprasad Ramaswami. "Design and Analysis of Consistent Algorithms for Multiclass Learning Problems." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3971.

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We consider the broad framework of supervised learning, where one gets examples of objects together with some labels (such as tissue samples labeled as cancerous or non-cancerous, or images of handwritten digits labeled with the correct digit in 0-9), and the goal is to learn a prediction model which given a new object, makes an accurate prediction. The notion of accuracy depends on the learning problem under study and is measured by a performance measure of interest. A supervised learning algorithm is said to be 'statistically consistent' if it returns an `optimal' prediction model with respe
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Harish, Guruprasad Ramaswami. "Design and Analysis of Consistent Algorithms for Multiclass Learning Problems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3971.

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We consider the broad framework of supervised learning, where one gets examples of objects together with some labels (such as tissue samples labeled as cancerous or non-cancerous, or images of handwritten digits labeled with the correct digit in 0-9), and the goal is to learn a prediction model which given a new object, makes an accurate prediction. The notion of accuracy depends on the learning problem under study and is measured by a performance measure of interest. A supervised learning algorithm is said to be 'statistically consistent' if it returns an `optimal' prediction model with respe
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Conference papers on the topic "Multiclass Evaluation Metrics"

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Zhukova, Kseniia, Miroslav Antonic, Mišo Soleša, and Dragan Camber. "Data-Driven Model for Measuring Hydraulic Fracturing Efficiency by Utilizing the Real-Time Treatment Data." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22384-ms.

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Abstract The paper presents a practical tool for hydraulic fracturing efficiency evaluation. The tool is based on a data-driven approach that helps in interpreting real-time data. Based on the hydraulic fracturing (HF) job monitoring, statistic metrics and key performance indicators (KPIs) are generated to be valuable input for further designs and identification of potential savings in operation. Machine learning (ML) algorithms are proposed to reduce the tedious work of completion engineers by automatically classifying each treatment schedule's timestamp and assigning the stage label. For ope
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Qalandari, Roohullah, Ruizhi Zhong, Cyrus Salehi, et al. "Estimation of Rock Permeability Scores Using Machine Learning Methods." In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210711-ms.

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Abstract Permeability is an important parameter that describes the flow characteristics of rocks (hydrocarbons in the oil and gas reservoirs or groundwater in aquifers). Currently, laboratory experiments using cored samples and well testing are the main methods to determine rock permeability. However, these methods are time-consuming and/or resource-intensive. This paper proposes a novel machine learning approach to predict permeability scores. Field drilling and wireline data are acquired from 80 wells in the Surat Basin, Australia. The permeability scores are based on petrophysical interpret
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