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Journal articles on the topic 'Multi-class classifiers'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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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R, B. Alemayehu, Rout M, and C. Satapathy S. "Supervised Learning-Based Prediction and Analysis of Amharic Twitter Data." Indian Journal of Science and Technology 16, no. 47 (2023): 4561–68. https://doi.org/10.17485/IJST/v16i47.591.

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Abstract <strong>Objectives:</strong> This study aims to prepare a corpus and explore sentiment analysis in the Amharic language, which is increasingly used due to the growth of both the language and the Internet. <strong>Methods:</strong> The study acquired 23,646 Amharic tweets from Twitter using the Twitter API, cleaned and normalized the text through preprocessing, and manually annotated the data as positive, negative, or neutral by three annotators. The study utilized a multi-scale sentiment analysis approach to experimentally evaluate the classifier's performance and compa

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Gautam, Suruchi, Sweety Ahlawat, and Prabhat Mittal. "Binary and Multi-class Classification of Brain Tumors using MRI Images." International Journal of Experimental Research and Review 29 (December 30, 2022): 1–9. http://dx.doi.org/10.52756/ijerr.2022.v29.001.

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A dangerous and potentially fatal condition is a brain tumor. Early detection of this disease is critical for determining the best course of treatment. Tumor detection and classification by human inspection is a time consuming, error-prone task involving huge amounts of data. Computer-assisted machine learning and image analysis techniques have achieved significant results in image processing. In this study, we use supervised and deep learning classifiers to detect and classify tumors using the MRI images from the BRATS 2020 dataset. At the outset, the proposed system classifies images as heal

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SHYU, MEI-LING, CHAO CHEN, and SHU-CHING CHEN. "MULTI-CLASS CLASSIFICATION VIA SUBSPACE MODELING." International Journal of Semantic Computing 05, no. 01 (2011): 55–78. http://dx.doi.org/10.1142/s1793351x1100116x.

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Aiming to build a satisfactory supervised classifier, this paper proposes a Multi-class Subspace Modeling (MSM) classification framework. The framework consists of three parts, namely Principal Component Classifier Training Array, Principal Component Classifier Testing Array, and Label Coordinator. The role of Principal Component Classifier Training Array is to get a set of optimized parameters and principal components from each subspace-based training classifier and pass them to the corresponding subspace-based testing classifier in Principal Component Classifier Testing Array. In each subspa

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Lin, Hung-Yi. "Efficient classifiers for multi-class classification problems." Decision Support Systems 53, no. 3 (2012): 473–81. http://dx.doi.org/10.1016/j.dss.2012.02.014.

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Siedlecki, Wojciech W. "A formula for multi-class distributed classifiers." Pattern Recognition Letters 15, no. 8 (1994): 739–42. http://dx.doi.org/10.1016/0167-8655(94)90001-9.

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Yi, Rumeng, Dayan Guan, Yaping Huang, and Shijian Lu. "Class-Independent Regularization for Learning with Noisy Labels." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3276–84. http://dx.doi.org/10.1609/aaai.v37i3.25434.

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Training deep neural networks (DNNs) with noisy labels often leads to poorly generalized models as DNNs tend to memorize the noisy labels in training. Various strategies have been developed for improving sample selection precision and mitigating the noisy label memorization issue. However, most existing works adopt a class-dependent softmax classifier that is vulnerable to noisy labels by entangling the classification of multi-class features. This paper presents a class-independent regularization (CIR) method that can effectively alleviate the negative impact of noisy labels in DNN training. C

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Abady, Lydia, Giovanna Maria Dimitri, and Mauro Barni. "A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images." Remote Sensing 16, no. 5 (2024): 781. http://dx.doi.org/10.3390/rs16050781.

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The current image generative models have achieved a remarkably realistic image quality, offering numerous academic and industrial applications. However, to ensure these models are used for benign purposes, it is essential to develop tools that definitively detect whether an image has been synthetically generated. Consequently, several detectors with excellent performance in computer vision applications have been developed. However, these detectors cannot be directly applied as they areto multi-spectral satellite images, necessitating the training of new models. While two-class classifiers gene

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H., Hartono, Risyani Yeni, Ongko Erianto, and Abdullah Dahlan. "HAR-MI method for multi-class imbalanced datasets." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 822–29. https://doi.org/10.12928/TELKOMNIKA.v18i2.14818.

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Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble se

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Zhao, Jiakun, Ju Jin, Yibo Zhang, Ruifeng Zhang, and Si Chen. "Multi-class WHMBoost: An ensemble algorithm for multi-class imbalanced data." Intelligent Data Analysis 26, no. 3 (2022): 599–614. http://dx.doi.org/10.3233/ida-215874.

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The imbalanced data problem is widespread in the real world. In the process of training machine learning models, ignoring imbalanced data problems will cause the performance of the model to deteriorate. At present, researchers have proposed many methods to deal with the imbalanced data problems, but these methods mainly focus on the imbalanced data problems in two-class classification tasks. Learning from multi-class imbalanced data sets is still an open problem. In this paper, an ensemble method for classifying multi-class imbalanced data sets is put forward, called multi-class WHMBoost. It i

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Zhao, Jiakun, Ju Jin, Yibo Zhang, Ruifeng Zhang, and Si Chen. "Multi-class WHMBoost: An ensemble algorithm for multi-class imbalanced data." Intelligent Data Analysis 26, no. 3 (2022): 599–614. http://dx.doi.org/10.3233/ida-215874.

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The imbalanced data problem is widespread in the real world. In the process of training machine learning models, ignoring imbalanced data problems will cause the performance of the model to deteriorate. At present, researchers have proposed many methods to deal with the imbalanced data problems, but these methods mainly focus on the imbalanced data problems in two-class classification tasks. Learning from multi-class imbalanced data sets is still an open problem. In this paper, an ensemble method for classifying multi-class imbalanced data sets is put forward, called multi-class WHMBoost. It i

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Bentkowska, Urszula, Wojciech Gałka, Marcin Mrukowicz, and Aleksander Wojtowicz. "Ensemble Classifier Based on Interval Modeling for Microarray Datasets." Entropy 26, no. 3 (2024): 240. http://dx.doi.org/10.3390/e26030240.

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The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier. Moreover, orders for intervals are applied to determ

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Dorozynski, M. "ADDRESSING CLASS IMBALANCE FOR TRAINING A MULTI-TASK CLASSIFIER IN THE CONTEXT OF SILK HERITAGE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 175–84. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-175-2023.

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Abstract. Collecting knowledge in the form of databases consisting of images and descriptive texts that represent objects from past centuries is a fundamental part of preserving cultural heritage. In this context, images with known information about depicted artifacts can serve as a source of information for automated methods to complete existing collections. For instance, image classifiers can provide predictions for different object properties (tasks) to semantically enrich collections. A challenge in this context is to train such classifiers given the nature of existing data: Many images do

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Xiao, Jie, Yunpeng Wang, and Hua Su. "Combining Support Vector Machines with Distance-based Relative Competence Weighting for Remote Sensing Image Classification: A Case Study." Journal of Imaging Science and Technology 64, no. 1 (2020): 10503–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2020.64.1.010503.

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Abstract A classification problem involving multi-class samples is typically divided into a set of two-class sub-problems. The pairwise probabilities produced by the binary classifiers are subsequently combined to generate a final result. However, only the binary classifiers that have been trained with the unknown real class of an unlabeled sample are relevant to the multi-class problem. A distance-based relative competence weighting (DRCW) combination mechanism can estimate the competence of the binary classifiers. In this work, we adapt the DRCW mechanism to the support vector machine (SVM)

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Kang, Seokho, Sungzoon Cho, and Pilsung Kang. "Multi-class classification via heterogeneous ensemble of one-class classifiers." Engineering Applications of Artificial Intelligence 43 (August 2015): 35–43. http://dx.doi.org/10.1016/j.engappai.2015.04.003.

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XU, XINYU, and BAOXIN LI. "MULTIPLE CLASS MULTIPLE-INSTANCE LEARNING AND ITS APPLICATION TO IMAGE CATEGORIZATION." International Journal of Image and Graphics 07, no. 03 (2007): 427–44. http://dx.doi.org/10.1142/s021946780700274x.

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We propose a Multiple Class Multiple-Instance (MCMI) learning approach and demonstrate its application to the problem of image categorization. Our method extends the binary Multiple-Instance learning approach for image categorization. Instead of constructing a set of binary classifiers (each trained to separate one category from the rest) and then making the final decision based on the winner of all the binary classifiers, our method directly allows the computation of a multi-class classifier by first projecting each training image onto a multi-class feature space and then simultaneously minim

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Mo, Lingfei, Lujie Zeng, Shaopeng Liu, and Robert X. Gao. "Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting." Information 10, no. 6 (2019): 197. http://dx.doi.org/10.3390/info10060197.

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This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performa

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Maximov, Yu, and D. Reshetova. "Tight risk bounds for multi-class margin classifiers." Pattern Recognition and Image Analysis 26, no. 4 (2016): 673–80. http://dx.doi.org/10.1134/s105466181604009x.

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D’Andrea, Eleonora, and Beatrice Lazzerini. "A hierarchical approach to multi-class fuzzy classifiers." Expert Systems with Applications 40, no. 9 (2013): 3828–40. http://dx.doi.org/10.1016/j.eswa.2012.12.097.

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Zhang, Boyuan, Wu Ma, Zhi Lu, and Bing Zeng. "Evolutionary Framework with Binary Decision Diagram for Multi-Classification: A Human-Inspired Approach." Electronics 14, no. 15 (2025): 2942. https://doi.org/10.3390/electronics14152942.

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Current mainstream classification methods predominantly employ end-to-end multi-class frameworks. These approaches encounter inherent challenges including high-dimensional feature space complexity, decision boundary ambiguity that escalates with increasing class cardinality, sensitivity to label noise, and limited adaptability to dynamic model expansion. However, human beings may avoid these mistakes naturally. Research indicates that humans subconsciously employ a decision-making process favoring binary outcomes, particularly when responding to questions requiring nuanced differentiation. Int

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Zhou, Shuang, Evgueni Nikolaevich Smirnov, and Ralf Peeters. "Conformal Region Classification with Instance-Transfer Boosting." International Journal on Artificial Intelligence Tools 24, no. 06 (2015): 1560002. http://dx.doi.org/10.1142/s0218213015600027.

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Conformal region classification focuses on developing region classifiers; i.e., classifiers that output regions (sets) of classes for new test instances.2,13,16 Conformal region classifiers have been proven to be valid for any significance level [Formula: see text] in the sense that the probability the class regions do not contain the true instances' classes does not exceed [Formula: see text]. In practice, however, conformal region classifiers need to be also efficient; i.e., they have to output non-empty and relatively small class regions. In this paper we show that conformal region classifi

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Buriro, Attaullah, Abdul Baseer Buriro, Tahir Ahmad, Saifullah Buriro, and Subhan Ullah. "MalwD&C: A Quick and Accurate Machine Learning-Based Approach for Malware Detection and Categorization." Applied Sciences 13, no. 4 (2023): 2508. http://dx.doi.org/10.3390/app13042508.

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Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify

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Krawczyk, Bartosz, Mikel Galar, Michał Woźniak, Humberto Bustince, and Francisco Herrera. "Dynamic ensemble selection for multi-class classification with one-class classifiers." Pattern Recognition 83 (November 2018): 34–51. http://dx.doi.org/10.1016/j.patcog.2018.05.015.

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Novotny, Alexander, George Bebis, Alireza Tavakkoli, and Mircea Nicolescu. "Ensembles of deep one-class classifiers for multi-class image classification." Machine Learning with Applications 19 (March 2025): 100621. https://doi.org/10.1016/j.mlwa.2025.100621.

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Kawadkar, Pankaj, Puppala Krupa Sagar, and B.Rebecca. "Multi-Class Support Vector Machines with Incremental Learning for Text Classification." International Journal of Scientific Methods in Intelligence Engineering Networks 01, no. 06 (2023): 36–44. http://dx.doi.org/10.58599/ijsmien.2023.1605.

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Class-incremental learning (CIL) is a revolutionary framework we develop in this study to address multi-class problems with support vector machines (SVM). Text classifiers built with support for support vector machines (SVMs) can be kept up-to-date with the help of CIL’s two incremental processes. Reusing previously learned classifier models, the CIL only needs to train a single binary sub-classifier and an extra step for feature assortmentonce a new class is introduced. The projections of the vectors onto the relevant subspaces are analyzed using the present classifier. Any text classificatio

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Raza, Ali, Furqan Rustam, Hafeez Ur Rehman Siddiqui, et al. "Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach." Genes 14, no. 1 (2022): 71. http://dx.doi.org/10.3390/genes14010071.

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions

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Ivanova, Krassimira, Iliya Mitov, and Peter L. Stanchev. "Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery." Digital Presentation and Preservation of Cultural and Scientific Heritage 1 (September 30, 2011): 117–26. http://dx.doi.org/10.55630/dipp.2011.1.13.

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Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common

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La, Lei, Qiao Guo, Dequan Yang, and Qimin Cao. "Multiclass Boosting with Adaptive Group-BasedkNN and Its Application in Text Categorization." Mathematical Problems in Engineering 2012 (2012): 1–24. http://dx.doi.org/10.1155/2012/793490.

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AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, andk-nearest neighbor (kNN). This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-basedkNN method is proposed in this paper to b

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Wang, Wuhua, Jiakui Tang, Na Zhang, Xuefeng Xu, Anan Zhang, and Yanjiao Wang. "Automated Detection Method to Extract Pedicularis Based on UAV Images." Drones 6, no. 12 (2022): 399. http://dx.doi.org/10.3390/drones6120399.

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Pedicularis has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract Pedicularis and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of mul

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CHATELAIN, CLEMENT, SEBASTIEN ADAM, YVES LECOURTIER, LAURENT HEUTTE, and THIERRY PAQUET. "NONCOST SENSITIVE SVM TRAINING USING MULTIPLE MODEL SELECTION." Journal of Circuits, Systems and Computers 19, no. 01 (2010): 231–42. http://dx.doi.org/10.1142/s0218126610005937.

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In this paper, we propose a multi-objective optimization framework for SVM hyperparameters tuning. The key idea is to manage a population of classifiers optimizing both False Positive and True Positive rates rather than a single classifier optimizing a scalar criterion. Hence, each classifier in the population optimizes a particular trade-off between the objectives. Within the context of two-class classification problems, our work introduces "the receiver operating characteristics (ROC) front concept" depicting a population of SVM classifiers as an alternative to the receiver operating charact

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Kang, Seokho, Sungzoon Cho, and Pilsung Kang. "Constructing a multi-class classifier using one-against-one approach with different binary classifiers." Neurocomputing 149 (February 2015): 677–82. http://dx.doi.org/10.1016/j.neucom.2014.08.006.

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Eslami, Elham, and Hae-Bum Yun. "Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images." Sensors 21, no. 15 (2021): 5137. http://dx.doi.org/10.3390/s21155137.

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Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encod

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Mei, Kuizhi, Ji Zhang, Guohui Li, Bao Xi, Nanning Zheng, and Jianping Fan. "Training more discriminative multi-class classifiers for hand detection." Pattern Recognition 48, no. 3 (2015): 785–97. http://dx.doi.org/10.1016/j.patcog.2014.09.001.

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Vluymans, Sarah, Dánel Sánchez Tarragó, Yvan Saeys, Chris Cornelis, and Francisco Herrera. "Fuzzy rough classifiers for class imbalanced multi-instance data." Pattern Recognition 53 (May 2016): 36–45. http://dx.doi.org/10.1016/j.patcog.2015.12.002.

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Radhika, K., and S. Varadarajan. "Satellite image classification and quality parame-ters using ML classifier." International Journal of Engineering & Technology 7, no. 1.8 (2018): 6. http://dx.doi.org/10.14419/ijet.v7i1.8.9441.

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Remote sensing images are an important source of information regarding the Earth surface. For many applications like geology, urban planning, forest and land cover/land use, the underlying information from such images is needed. Extraction of this information is usually achieved through a classification process which is one of the most powerful tools in digital image processing. Good classifier is required to extract the information in satellite images. Latest methods used for classification of pixels in multispectral satellite images are supervised classifiers such as Support Vector Machines

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Fragoso, Rogério C. P., George D. C. Cavalcanti, Roberto H. W. Pinheiro, and Luiz S. Oliveira. "Dynamic selection and combination of one-class classifiers for multi-class classification." Knowledge-Based Systems 228 (September 2021): 107290. http://dx.doi.org/10.1016/j.knosys.2021.107290.

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Lu, Yi Nan, Yong Quan, and Dan Mei Huang. "A Multiple Classifier System for Functional Classification of G Protein-Coupled Receptors." Advanced Materials Research 171-172 (December 2010): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.403.

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G protein-coupled receptors (GPCRs) are the largest protein super family in the human body and play a significant role in the pharmaceutical industry as drug targets. Although the deep research in GPCRs is continuing to grow, the study on their structures and functions is limited, for it is difficult and complex for the researchers to obtain purified membrane protein structures of GPCRs. Currently, how to get the structures of GPCRs and how to analyze them are the most important issues in modern molecular biology and medicine. We adopted multi-classifier method to deal with the problem of func

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Ropelewska, Ewa. "The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium." Agricultural Engineering 22, no. 3 (2018): 49–56. http://dx.doi.org/10.1515/agriceng-2018-0026.

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AbstractThe aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100

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Qin, Yu Ping, Peng Da Qin, Yi Wang, and Shu Xian Lun. "A New Optimal Binary Tree SVM Multi-Class Classification Algorithm." Applied Mechanics and Materials 373-375 (August 2013): 1085–88. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1085.

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A improved binary tree SVM multi-class classification algorithm is proposed. Firstly, constructing the minimum hyper ellipsoid for each class sample in the feather space, and then generating optimal binary tree according to the hyper ellipsoid volume, training sub-classifier for every non-leaf node in the binary tree at the same time. For the sample to be classified, the sub-classifiers are used from the root node until one leaf node, and the corresponding class of the leaf node is the class of the sample. The experiments are done on the Statlog database, and the experimental results show that

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Maximov, Yury, Massih-Reza Amini, and Zaid Harchaoui. "Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm." Journal of Artificial Intelligence Research 61 (April 11, 2018): 761–86. http://dx.doi.org/10.1613/jair.5638.

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We propose Rademacher complexity bounds for multi-class classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing k predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold stands for clustering consistency. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the

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Wang, Changlin, Zhixia Yang, Junyou Ye, and Xue Yang. "Kernel-Free Quadratic Surface Regression for Multi-Class Classification." Entropy 25, no. 7 (2023): 1103. http://dx.doi.org/10.3390/e25071103.

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For multi-class classification problems, a new kernel-free nonlinear classifier is presented, called the hard quadratic surface least squares regression (HQSLSR). It combines the benefits of the least squares loss function and quadratic kernel-free trick. The optimization problem of HQSLSR is convex and unconstrained, making it easy to solve. Further, to improve the generalization ability of HQSLSR, a softened version (SQSLSR) is proposed by introducing an ε-dragging technique, which can enlarge the between-class distance. The optimization problem of SQSLSR is solved by designing an alteration

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