Relevant bibliographies by topics / Multi-class classifiers

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Academic literature on the topic 'Multi-class classifiers'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Multi-class classifiers"

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Bo, Shukui, and Yongju Jing. "Data Distribution Partitioning for One-Class Extraction from Remote Sensing Imagery." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (2017): 1754018. http://dx.doi.org/10.1142/s0218001417540180.

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One-class extraction from remotely sensed imagery is researched with multi-class classifiers in this paper. With two supervised multi-class classifiers, Bayesian classifier and nearest neighbor classifier, we firstly analyzed the effect of the data distribution partitioning on one-class extraction from the remote sensing images. The data distribution partitioning refers to the way that the data set is partitioned before classification. As a parametric method, the Bayesian classifier achieved good classification performance when the data distribution was partitioned appropriately. While as a no
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Bourke, Chris, Kun Deng, Stephen D. Scott, Robert E. Schapire, and N. V. Vinodchandran. "On reoptimizing multi-class classifiers." Machine Learning 71, no. 2-3 (2008): 219–42. http://dx.doi.org/10.1007/s10994-008-5056-8.

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Abdallah, Loai, Murad Badarna, Waleed Khalifa, and Malik Yousef. "MultiKOC: Multi-One-Class Classifier Based K-Means Clustering." Algorithms 14, no. 5 (2021): 134. http://dx.doi.org/10.3390/a14050134.

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In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based
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Mahmood, Zafar, Naveed Anwer Butt, Ghani Ur Rehman, et al. "Generation of Controlled Synthetic Samples and Impact of Hyper-Tuning Parameters to Effectively Classify the Complex Structure of Overlapping Region." Applied Sciences 12, no. 16 (2022): 8371. http://dx.doi.org/10.3390/app12168371.

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The classification of imbalanced and overlapping data has provided customary insight over the last decade, as most real-world applications comprise multiple classes with an imbalanced distribution of samples. Samples from different classes overlap near class boundaries, creating a complex structure for the underlying classifier. Due to the imbalanced distribution of samples, the underlying classifier favors samples from the majority class and ignores samples representing the least minority class. The imbalanced nature of the data—resulting in overlapping regions—greatly affects the learning of
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Sultana, Jabeen, Abdul Khader Jilani, and . "Predicting Breast Cancer Using Logistic Regression and Multi-Class Classifiers." International Journal of Engineering & Technology 7, no. 4.20 (2018): 22. http://dx.doi.org/10.14419/ijet.v7i4.20.22115.

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The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the d
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SATHYAMANGALAM NATARAJAN, SHIVAPPRIYA, ARUN KUMAR SHANMUGAM, JUDE HEMANTH DURAISAMY, and HARIKUMAR RAJAGURU. "PREDICTION OF CARDIAC ARRHYTHMIA USING MULTI CLASS CLASSIFIERS BY INCORPORATING WAVELET TRANSFORM BASED FEATURES." DYNA 97, no. 4 (2022): 418–24. http://dx.doi.org/10.6036/10458.

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Timely diagnosis and earlier detection of the dangerous heart conditions will reduce the mortality rate and save life of the patient. For that, it is necessary to automate the classi?cation and prediction of Cardiac Arrhythmia. Raw ECG signal is extracted from the MIT-BIH Arrhythmia database, followed by preprocessing and feature extraction using wavelet transform method. Further the extracted features are used for the classification of four different cardiac arrhythmias such as Bradycardia, Tachycardia, Left and Right Bundle Branch Block. Comparative study on the five different classifiers na
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Liu, Jinfu, Mingliang Bai, Na Jiang, et al. "Interclass Interference Suppression in Multi-Class Problems." Applied Sciences 11, no. 1 (2021): 450. http://dx.doi.org/10.3390/app11010450.

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Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on intercl
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Kumar, Amit, and Anand Shanker Tewari. "Risk Identification of Diabetic Macular Edema Using E-Adoption of Emerging Technology." International Journal of E-Adoption 14, no. 3 (2022): 1–20. http://dx.doi.org/10.4018/ijea.310000.

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The accumulation of the blood leaks on the retina is known as diabetic macular edema (DME), which can result in irreversible blindness. Early diagnosis and therapy can stop DME. This study presents an e-adoption of emerging technology such as RadioDense model for detecting and classifying DME from retinal fundus images. The proposed model employs a modified version of DenseNet121, radiomics features, and the gradient boosting classifier. The authors evaluated many classifiers on the concatenated features. The efficacy of the classifier is determined by comparing each classifier's accuracy valu
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Zhang, Yu-Yang, Bin-Bin Jia, and Min-Ling Zhang. "Evolutionary Classifier Chain for Multi-Dimensional Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22641–49. https://doi.org/10.1609/aaai.v39i21.34423.

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In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order op
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Pal, Mahendra, Thorkild Rasmussen, and Alok Porwal. "Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers." Remote Sensing 12, no. 1 (2020): 177. http://dx.doi.org/10.3390/rs12010177.

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Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the cla
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Dissertations / Theses on the topic "Multi-class classifiers"

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Kybartas, Rimantas. "Multi-class recognition using pair-wise classifiers." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150424-92661.

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There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the seco
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Abd, Rahman Mohd Amiruddin. "Kernel and multi-class classifiers for multi-floor WLAN localisation." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13768/.

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Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and time-efficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multi-floor environment. Thi
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Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.
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Odabai, Fard Seyed Hamidreza. "Efficient multi-class objet detection with a hierarchy of classes." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22623/document.

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Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à
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Verschae, Tannenbaum Rodrigo. "Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case." Tesis, Universidad de Chile, 2010. http://www.repositorio.uchile.cl/handle/2250/102398.

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Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different superv
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Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.

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Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number o
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Son, Kyung-Im. "A multi-class, multi-dimensional classifier as a topology selector for analog circuit design / by Kyung-Im Son." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5919.

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Bautista, Martín Miguel Ángel. "Learning error-correcting representations for multi-class problems." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396124.

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Real life is full of multi-class decision tasks. In the Pattern Recognition field, several method- ologies have been proposed to deal with binary problems obtaining satisfying results in terms of performance. However, the extension of very powerful binary classifiers to the multi-class case is a complex task. The Error-Correcting Output Codes framework has demonstrated to be a very powerful tool to combine binary classifiers to tackle multi-class problems. However, most of the combinations of binary classifiers in the ECOC framework overlook the underlay- ing structure of the multi-class problem.
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Rocha, Anderson de Rezende 1980. "Classificadores e aprendizado em processamento de imagens e visão computacional." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276019.

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Orientador: Siome Klein Goldenstein<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto da Computação<br>Made available in DSpace on 2018-08-12T17:37:15Z (GMT). No. of bitstreams: 1 Rocha_AndersondeRezende_D.pdf: 10303487 bytes, checksum: 243dccfe5255c828ce7ead27c27eb1cd (MD5) Previous issue date: 2009<br>Resumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional.
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Books on the topic "Multi-class classifiers"

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Pathak, Sudhir, and Soudamini Hota. KNN Classifier Based Approach for Multi-Class Sentiment Analysis of Twitter Data. Independently Published, 2017.

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Book chapters on the topic "Multi-class classifiers"

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Xiao, Han, Thomas Stibor, and Claudia Eckert. "Evasion Attack of Multi-class Linear Classifiers." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_18.

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Roth, Volker. "Probabilistic Discriminative Kernel Classifiers for Multi-Class Problems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45404-7_33.

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Hadjadji, Bilal, Youcef Chibani, and Hassiba Nemmour. "Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_35.

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Raudys, Sarunas, Vitalij Denisov, and Antanas Andrius Bielskis. "A Pool of Classifiers by SLP: A Multi-class Case." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867661_5.

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San, Cho Cho, Mie Mie Su Thwin, and Naing Linn Htun. "Malicious Software Family Classification using Machine Learning Multi-class Classifiers." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2622-6_41.

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Takenouchi, Takashi, and Shin Ishii. "A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34481-7_46.

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Schirra, Lyn-Rouven, Florian Schmid, Hans A. Kestler, and Ludwig Lausser. "Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorization." In Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46182-3_9.

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Hirasawa, Shigeichi, Gendo Kumoi, Manabu Kobayashi, Masayuki Goto, and Hiroshige Inazumi. "System Evaluation of Construction Methods for Multi-class Problems Using Binary Classifiers." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_86.

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Huk, Maciej. "Avoiding Time Series Prediction Disbelief with Ensemble Classifiers in Multi-class Problem Spaces." In Intelligent Information and Database Systems. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21967-2_13.

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Yukinawa, Naoto, Shigeyuki Oba, Kikuya Kato, and Shin Ishii. "Multi-class Pattern Classification Based on a Probabilistic Model of Combining Binary Classifiers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_54.

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Conference papers on the topic "Multi-class classifiers"

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AJIMOTO, Kensuke, Yuma YAMAMOTO, Yoshifumi KUSUNOKI, and Tomoharu NAKASHIMA. "A Study on Multi-Class Online Fuzzy Classifiers for Dynamic Environments." In 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2024. http://dx.doi.org/10.1109/fuzz-ieee60900.2024.10612027.

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Jyothi, Talapaneni, Bipin Bihari Jayasingh, Thatikonda Radhika, and A. Soujanya. "Deep Learning Classifiers for Multi-Class Classification of Medical Images using Chest X-Ray Scans." In 2024 First International Conference on Innovations in Communications, Electrical and Computer Engineering (ICICEC). IEEE, 2024. https://doi.org/10.1109/icicec62498.2024.10808266.

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Wang, Kai, Fei Yang, Bogdan Raducanu, and Joost van de Weijer. "Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00432.

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Kim, Bokyung, Qijia Huang, Brady Taylor, et al. "MulPi: A Multi-Class and Patient-Independent Computing-in-SRAM Seizure Classifier." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798153.

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Xu, Jie, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, and Heng Huang. "Multi-Class Support Vector Machine via Maximizing Multi-Class Margins." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/440.

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Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subpr
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Gautam, Chandan, Aruna Tiwari, and Sriram Ravindran. "Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727445.

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Tao Ban and S. Abe. "Implementing Multi-class Classifiers by One-class Classification Methods." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246699.

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Mai, Tien-Dung, Thanh Duc Ngo, Duy-Dinh Le, Duc Anh Duong, Kiem Hoang, and Shin'ichi Satoh. "Large scale multi-class classification using latent classifiers." In 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2015. http://dx.doi.org/10.1109/mmsp.2015.7340800.

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Kumar, Himanshu, and P. S. Sastry. "Robust Loss Functions for Learning Multi-class Classifiers." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00125.

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Traganitis, Panagiotis A., and Georgios B. Giannakis. "Blind Multi-class Ensemble Learning with Dependent Classifiers." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553113.

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