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Relevant bibliographies by topics / Multi class ordinal classification

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Academic literature on the topic 'Multi class ordinal classification'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Multi class ordinal classification"

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Bellmann, Peter, Ludwig Lausser, Hans A. Kestler, and Friedhelm Schwenker. "A Theoretical Approach to Ordinal Classification: Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures." Applied Sciences 12, no. 4 (2022): 1815. http://dx.doi.org/10.3390/app12041815.

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Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a comm

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Xu, Xinzheng, Qiaoyu Guo, Zhongnian Li, and Dechun Li. "Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis." Healthcare 10, no. 11 (2022): 2300. http://dx.doi.org/10.3390/healthcare10112300.

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Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesi

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LV, JIA, and NAIYANG DENG. "MULTI-CLASS TRANSDUCTIVE CLASSIFICATION BASED ON LOCAL LEARNING AND ADJUSTABLE CLASS LABEL REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 06 (2012): 1250014. http://dx.doi.org/10.1142/s0218001412500140.

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Local learning has been successfully applied to transductive classification problems. In this paper, it is generalized to multi-class classification of transductive learning problems owing to its good classification ability. Meanwhile, there is essentially no ordinal meaning in class label of multi-class classification, and it belongs to discrete nominal variable. However, common binary series class label representation has the equal distance from one class to another, and it does not reflect the sparse and density relationship among classes distribution, so a learning and adjustable nominal c

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N. Shah Zainudin, M., Md Nasir Sulaiman, Norwati Musapha, Thinagaran Perumal, and Raihani Mohamed. "Solving Classification Problem Using Ensemble Binarization Classifier." International Journal of Engineering & Technology 7, no. 4.31 (2018): 280–84. http://dx.doi.org/10.14419/ijet.v7i4.31.23381.

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Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classif

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Jin, David, Ariel Kapusta, Patrick A. Minot, et al. "A Hazard-Aware Metric for Ordinal Multi-Class Classification in Pathology." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 236–38. http://dx.doi.org/10.1609/aaaiss.v2i1.27680.

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Artificial Intelligence (AI) for decision support and diagnosis in pathology could provide immense value to society, improving patient outcomes and alleviating workload demands on pathologists. However, this potential cannot be realized until sufficient methods for testing and evaluation of such AI systems are developed and adopted. We present a novel metric for evaluation of multi-class classification algorithms for pathology, Error Severity Index (ESI), to address the needs of pathologists and pathology lab managers in evaluating AI systems.

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Mondonneix, Gaël, Jean Martial Mari, Sébastien Chabrier, and Alban Gabillon. "A kernel machine for hidden object-ranking problems (HORPs)." Multimedia Tools and Applications 79, no. 47-48 (2020): 35093–107. http://dx.doi.org/10.1007/s11042-020-09184-y.

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AbstractHidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clu

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de la Torre, Jordi, Domenec Puig, and Aida Valls. "Weighted kappa loss function for multi-class classification of ordinal data in deep learning." Pattern Recognition Letters 105 (April 2018): 144–54. http://dx.doi.org/10.1016/j.patrec.2017.05.018.

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Lin, Hung-Yi. "Feature selection based on cluster and variability analyses for ordinal multi-class classification problems." Knowledge-Based Systems 37 (January 2013): 94–104. http://dx.doi.org/10.1016/j.knosys.2012.07.018.

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Bartley, Christopher, Wei Liu, and Mark Reynolds. "Enhanced Random Forest Algorithms for Partially Monotone Ordinal Classification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3224–31. http://dx.doi.org/10.1609/aaai.v33i01.33013224.

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One of the factors hindering the use of classification models in decision making is that their predictions may contradict expectations. In domains such as finance and medicine, the ability to include knowledge of monotone (nondecreasing) relationships is sought after to increase accuracy and user satisfaction. As one of the most successful classifiers, attempts have been made to do so for Random Forest. Ideally a solution would (a) maximise accuracy; (b) have low complexity and scale well; (c) guarantee global monotonicity; and (d) cater for multi-class. This paper first reviews the state-of-t

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Chen, Cathy W. S., and L. M. Chiu. "Ordinal Time Series Forecasting of the Air Quality Index." Entropy 23, no. 9 (2021): 1167. http://dx.doi.org/10.3390/e23091167.

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This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogen

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Dissertations / Theses on the topic "Multi class ordinal classification"

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Wong, Chi Man. "Extreme learning machine for multi-class classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948432.

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Rennie, Jason D. M. (Jason Daniel Malyutin) 1976. "Improving multi-class text classification with Naive Bayes." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86780.

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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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Hallsmar, Fredrik, and Jonas Palm. "Multi-class Sentiment Classification on Twitter using an Emoji Training Heuristic." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186369.

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Sentiment analysis on social media is an important part of today's need for information gathering. Different machine learning techniques have been used in recent years, and usage of an emoticon heuristic to automatically annotate training sets has been a popular approach. As emojis are becoming more popular to use in text-based communication this thesis investigates the feasibility of an emoji training heuristic for multi-class sentiment analysis using a Multinomial Naive Bayes Classifier. Training sets consisting of 4000 to 400 000 tweets were used to train the classifier using various config

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Pan, Zhiwei. "Statistical learning algorithms : multi-class classification and regression with non-i.i.d. sampling /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b30082316f.pdf.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [65]-75)

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CHAVES, ADRIANA DA COSTA FERREIRA. "FUZZY RULES EXTRACTION FROM SUPPORT VECTOR MACHINES (SVM) FOR MULTI-CLASS CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9191@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Este trabalho apresenta a proposta de um novo método para a extração de regras fuzzy de máquinas de vetor suporte (SVMs) treinadas para problemas de classificação. SVMs são sistemas de aprendizado baseados na teoria estatística do aprendizado e apresentam boa habilidade de generalização em conjuntos de dados reais. Estes sistemas obtiveram sucesso em vários tipos de problemas. Entretanto, as SVMs, da mesma forma que redes neurais (RN), geram um modelo caixa preta, isto é, um modelo que não explica o processo pelo qual sua

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Xue, Yongjian. "Dynamic Transfer Learning for One-class Classification : a Multi-task Learning Approach." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0006.

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Le but de cette thèse est de minimiser la perte de performance d'un système de détection lorsqu'il rencontre un changement de distribution de données à la suite d’un événement connu (maintenance, ajout de capteur etc.). L'idée est d'utiliser l'approche d'apprentissage par transfert pour exploiter l'information apprise avant l’événement pour adapter le détecteur au système modifié. Un modèle d'apprentissage multitâche est proposé pour résoudre ce problème. Il utilise un paramètre pour équilibrer la quantité d'informations apportées par l'ancien système par rapport au nouveau. Ce modèle est form

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Gladh, Marcus, and Daniel Sahlin. "Image Synthesis Using CycleGAN to Augment Imbalanced Data for Multi-class Weather Classification." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176991.

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In the last decade, convolutional neural networks have been used to a large extent for image classification and recognition tasks in a number of fields. For image weather classification, data can be both sparse and unevenly distributed amongst labels in the training set. As a way to improve the performance of the classifier, one often used traditional augmentation techniques to increase the size of the training set and help the classifier to converge towards a desirable solution. This can often be met with varying results, which is why this work intends to investigate another approach of augme

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Benites, de Azevedo e. Souza Fernando [Verfasser]. "Multi-label Classification with Multiple Class Ontologies / Fernando Benites de Azevedo e Souza." Konstanz : Bibliothek der Universität Konstanz, 2017. http://d-nb.info/114951048X/34.

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Andersson, Melanie. "Multi-Class Imbalanced Learning for Time Series Problem : An Industrial Case Study." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412799.

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Classification problems with multiple classes and imbalanced sample sizes present a new challenge than the binary classification problems. Methods have been proposed to handle imbalanced learning, however most of them are specifically designed for binary classification problems. Multi-class imbalance imposes additional challenges when applied to time series classification problems, such as weather classification. In this thesis, we introduce, apply and evaluate a new algorithm for handling multi-class imbalanced problems involving time series data. Our proposed algorithm is designed to handle

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Books on the topic "Multi class ordinal classification"

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Chakraborty, Sanjay, and Lopamudra Dey. Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9622-9.

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Knaup, Julian. Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-38955-0.

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Knaup, Julian. Impact of Class Assignment on Multinomial Classification Using Multi-Valued Neurons. Springer Fachmedien Wiesbaden GmbH, 2022.

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Book chapters on the topic "Multi class ordinal classification"

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Jiang, Zhiwei, Gang Sun, Qing Gu, and Daoxu Chen. "An Ordinal Multi-class Classification Method for Readability Assessment of Chinese Documents." In Knowledge Science, Engineering and Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12096-6_6.

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Chakraborty, Sanjay, and Lopamudra Dey. "Multi-class Classification." In Springer Tracts in Nature-Inspired Computing. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9622-9_3.

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Antoniuk, Kostiantyn, Vojtěch Franc, and Václav Hlaváč. "MORD: Multi-class Classifier for Ordinal Regression." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40994-3_7.

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Fuangkhon, Piyabute, and Thitipong Tanprasert. "Multi-class Contour Preserving Classification." In Intelligent Data Engineering and Automated Learning - IDEAL 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_5.

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Mohr, Felix, Marcel Wever, and Eyke Hüllermeier. "Reduction Stumps for Multi-class Classification." In Advances in Intelligent Data Analysis XVII. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01768-2_19.

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Balikas, Georgios, and Massih-Reza Amini. "Multi-label, Multi-class Classification Using Polylingual Embeddings." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30671-1_59.

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Gashi, Shkurta, Elena Di Lascio, and Silvia Santini. "Multi-class Multi-label Classification for Cooking Activity Recognition." In Human Activity Recognition Challenge. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8269-1_7.

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Xu, Yan, Yuanhai Shao, Yingjie Tian, and Naiyang Deng. "Linear Multi-class Classification Support Vector Machine." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02298-2_93.

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Mayoraz, Eddy, and Ethem Alpaydin. "Support vector machines for multi-class classification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100551.

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Moosaei, Hossein, and Milan Hladík. "Least Squares K-SVCR Multi-class Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53552-0_13.

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Conference papers on the topic "Multi class ordinal classification"

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Artag, Jargalsaikhan, Moe Shimada, and Jun-ichi Shirakashi. "Multi-Task Quantum Annealing for Rapid Multi-Class Classification." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10378.

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P, Sharmila S., Aruna Tiwari, and Narendra S. Chaudhari. "Obfuscated Malware Detection Using Multi-Class Classification." In 2023 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2023. http://dx.doi.org/10.1109/ccem60455.2023.00034.

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Effendie, Michela, and Vanessa Aguiar-Pulido. "Multi-Modal AI Approach for Multi-Class Skin Disease Classification." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00245.

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Zheng, Suyang, Kai Zhou, and Chouyong Chen. "Perturbation-Based SMOTE for Multi-Class Imbalanced Classification." In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA). IEEE, 2024. http://dx.doi.org/10.1109/icmlca63499.2024.10753837.

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Özdogan, Miran, Alan Jeffares, and Sean Holden. "Partition Tree Ensembles for Improving Multi-Class Classification." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013163400003905.

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Alfaham, Abdallah, Stijn Van Raemdonck, and Siegfried Mercelis. "Genetic NEAT-Based Method for Multi-Class Classification." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899662.

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Sherif, Mennatallah, Hani Attar, Mohamed A. Hafez, Jafar Ababneh, Hussein Al-Faiz, and Mohanad A. Deif. "Multi-class classification algorithm for ocular diseases classification based GLCM texture features." In 2024 25th International Arab Conference on Information Technology (ACIT). IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877001.

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Waegeman, Willem, and Bernard De Baets. "ERA ranking representability: The missing link between ordinal regression and multi-class classification." In 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2011. http://dx.doi.org/10.1109/isda.2011.6121820.

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Cruz, Ricardo, Margarida Silveira, and Jaime S. Cardoso. "A Class Imbalance Ordinal Method for Alzheimer’s Disease Classification." In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI). IEEE, 2018. http://dx.doi.org/10.1109/prni.2018.8423960.

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Huo, Zengwei, and Xin Geng. "Ordinal Zero-Shot Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/266.

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Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use. Fortunately in many classification tasks, the class labels are ordered, and therefore closely related to each other. This paper deals with zero-shot learning for ordinal classification. The key idea is using label relevance to expand supervision information from seen labels to unseen labels. The proposed method SIDL generates a su

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Reports on the topic "Multi class ordinal classification"

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Barker, Kash, Theodore B. Trafalis, and Cameron A. MacKenzie. Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada595533.

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Green, Andre. LUNA Condition Based Monitoring Update: Using Minimum of Mahalanobis Distances for Multi-Class Classification. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1808794.

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Yeung, Ka Y., Roger E. Bumgarner, and Adrian E. Raftery. Bayesian Model Averaging: Development of an Improved Multi-Class, Gene Selection and Classification Tool for Microarray Data. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada454826.

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Delwiche, Michael, Yael Edan, and Yoav Sarig. An Inspection System for Sorting Fruit with Machine Vision. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7612831.bard.

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Concepts for real-time grading of fruits and vegetables were developed, including multi-spectral imaging with structured illumination to detect and distinguish surface defects from concavities. Based on these concepts, a single-lane conveyor and inspection system were designed and evaluated. Image processing algorithms were developed to inspect and grade large quasi-spherical fruits (peaches and apples) and smaller dried fruits (dates). Adjusting defect pixel thresholds to achieve a 25% error rate on good apples, classification errors for bruise, crack, and cut classes were 51%, 42%, and 46%,

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p

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