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Journal articles on the topic 'Error Correcting Output Code (ECOC)'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Kajdanowicz, Tomasz, and Przemysław Kazienko. "Multi-label classification using error correcting output codes." International Journal of Applied Mathematics and Computer Science 22, no. 4 (2012): 829–40. http://dx.doi.org/10.2478/v10006-012-0061-2.

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A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code match

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K. Patel, Rinkal, and Irfan Poladi. "A STUDY PAPER ON ERROR CORRECTING OUTPUT CODE BUILD ON MULTICLASS CLASSIFICATION." International Journal of Engineering Applied Sciences and Technology 7, no. 10 (2023): 124–28. http://dx.doi.org/10.33564/ijeast.2023.v07i10.016.

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Error-correcting output codes is a technique for using binary classification models on multi-class classification prediction tasks. Error-Correcting Output Codes (ECOC) represents a successful framework to deal with these kinds of problems. Recent works in the ECOC framework appear notable performance improvements. The ECOC framework is a high-level tool to deal with multi-class categorization problems. As the error correcting output codes have error correcting ability and improve the generalization ability to base classification. This library contains both modern coding (one-versus-one, one-v

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Windridge, David, Riccardo Mengoni, and Rajagopal Nagarajan. "Quantum error-correcting output codes." International Journal of Quantum Information 16, no. 08 (2018): 1840003. http://dx.doi.org/10.1142/s0219749918400038.

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Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this pape

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Wang, Li-Na, Hongxu Wei, Yuchen Zheng, Junyu Dong, and Guoqiang Zhong. "Deep Error-Correcting Output Codes." Algorithms 16, no. 12 (2023): 555. http://dx.doi.org/10.3390/a16120555.

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Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval. In this paper, combining the ideas of ensemble learning, online learning and deep learning, we propose a novel deep learning method called deep error-correcting output codes (DeepECOCs). DeepECOCs are composed of multiple layers of the ECOC module, which combines several incremental support vector machines (incremental SVMs) as base classifiers. In this novel deep architecture, each ECOC module can be

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Utschick, Wolfgang, and Werner Weichselberger. "Stochastic Organization of Output Codes in Multiclass Learning Problems." Neural Computation 13, no. 5 (2001): 1065–102. http://dx.doi.org/10.1162/08997660151134334.

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The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective fun

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Ekta, Soni, Nagpal Arpita, and Chopra Khyati. "Atrial Fibrillation Discrimination for Real-Time ECG Monitoring Based On QT Interval Variation." Indian Journal of Science and Technology 15, no. 17 (2022): 767–77. https://doi.org/10.17485/IJST/v15i17.53.

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Abstract <strong>Background/Objectives:</strong> An occasional Atrial Fibrillation (AF) event in heart rhythm should be monitored regularly, in continuous intervals. Timely detection of these anomalies in heart rhythm is required to save patients from sudden cardiac arrest. <strong>Method:</strong> A long-duration ECG categorization algorithm named AFECOC is proposed. For this one-minute-long 71 signals are attained from the Physionet’s “MIT-BIH arrhythmia (MA)” and “AF” database. Two-stage filtering of noisy signals is employed before signal analys

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Zhang, Bowen, Benedetta Tondi, Xixiang Lv, and Mauro Barni. "Challenging the Adversarial Robustness of DNNs Based on Error-Correcting Output Codes." Security and Communication Networks 2020 (November 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/8882494.

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The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defence mechanisms. The use of networks adopting error-correcting output codes (ECOC) has recently been proposed to counter the creation of adversarial examples in a white-box setting. In this paper, we carry out an in-depth investigation of the adversarial robustness achieved by the ECOC approach. We do so by proposing a new adversarial attack specifically designed for multilabel classification arc

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Joutsijoki, Henry, Markus Haponen, Jyrki Rasku, Katriina Aalto-Setälä, and Martti Juhola. "Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images." BioMed Research International 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/3025057.

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The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems. We focus on four different coding designs of ECOC and apply to each one of themk-Nearest Neighbor (k-NN) searching, naïve Bayes, classification tree, and discriminant analysis variants classifiers. We use

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Ciompi, Francesco, Oriol Pujol, and Petia Radeva. "ECOC-DRF: Discriminative random fields based on error correcting output codes." Pattern Recognition 47, no. 6 (2014): 2193–204. http://dx.doi.org/10.1016/j.patcog.2013.12.007.

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Lei, Lei, Yafei Song, and Xi Luo. "A new re-encoding ECOC using reject option." Applied Intelligence 50, no. 10 (2020): 3090–100. http://dx.doi.org/10.1007/s10489-020-01642-2.

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Abstract When training base classifier by ternary Error Correcting Output Codes (ECOC), it is well know that some classes are ignored. On this account, a non-competent classifier emerges when it classify an instance whose real label does not belong to the meta-subclasses. Meanwhile, the classic ECOC dichotomizers can only produce binary outputs and have no capability of rejection for classification. To overcome the non-competence problem and better model the multi-class problem for reducing the classification cost, we embed reject option to ECOC and present a new variant of ECOC algorithm call

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Song, Yang, Qiyu Kang, and Wee Peng Tay. "Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9722–29. http://dx.doi.org/10.1609/aaai.v35i11.17169.

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Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi-class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simulta

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Sarabia, Pablo, Alvaro Araujo, Luis Antonio Sarabia, and María de la Cruz Ortiz. "Electromyography Gesture Model Classifier for Fault-Tolerant-Embedded Devices by Means of Partial Least Square Class Modelling Error Correcting Output Codes (PLS-ECOC)." Algorithms 16, no. 3 (2023): 149. http://dx.doi.org/10.3390/a16030149.

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Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human–machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm imposes strict requirements on the complexity classifier. To date, research on hand gesture recognition (GR) based on sEMG uses discriminant classifiers, such as support vector machines and neural networks. These classifiers can achieve good precision; they cannot detect when an

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Pujol, O., P. Radeva, and J. Vitria. "Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 6 (2006): 1007–12. http://dx.doi.org/10.1109/tpami.2006.116.

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Chen, Ling-qing, Mei-ting Wu, Li-fang Pan, and Ru-bin Zheng. "Grade Prediction in Blended Learning Using Multisource Data." Scientific Programming 2021 (September 11, 2021): 1–15. http://dx.doi.org/10.1155/2021/4513610.

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Today, blended learning is widely carried out in many colleges. Different online learning platforms have accumulated a large number of fine granularity records of students’ learning behavior, which provides us with an excellent opportunity to analyze students’ learning behavior. In this paper, based on the behavior log data in four consecutive years of blended learning in a college’s programming course, we propose a novel multiclassification frame to predict students’ learning outcomes. First, the data obtained from diverse platforms, i.e., MOOC, Cnblogs, Programming Teaching Assistant (PTA) s

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Manhando, Edwin, Yang Zhou, and Fenglin Wang. "Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography." AgriEngineering 3, no. 3 (2021): 703–15. http://dx.doi.org/10.3390/agriengineering3030045.

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Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vecto

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Xie, Shu-tong, Qiong Chen, Kun-hong Liu, Qing-zhao Kong, and Xiu-juan Cao. "Learning Behavior Analysis Using Clustering and Evolutionary Error Correcting Output Code Algorithms in Small Private Online Courses." Scientific Programming 2021 (June 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/9977977.

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In recent years, online and offline teaching activities have been combined by the Small Private Online Course (SPOC) teaching activities, which can achieve a better teaching result. Therefore, colleges around the world have widely carried out SPOC-based blending teaching. Particularly in this year’s epidemic, the online education platform has accumulated lots of education data. In this paper, we collected the student behavior log data during the blending teaching process of the “College Information Technology Fundamentals” course of three colleges to conduct student learning behavior analysis

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Lyu, Lu, and Yong Huang. "Sports activity (SA) recognition based on error correcting output codes (ECOC) and convolutional neural network (CNN)." Heliyon 10, no. 6 (2024): e28258. http://dx.doi.org/10.1016/j.heliyon.2024.e28258.

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Yang, Rongchao, and Jiangming Kan. "Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images." Remote Sensing 14, no. 6 (2022): 1524. http://dx.doi.org/10.3390/rs14061524.

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This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, and to mine a more effective hyperspectral identification algorithm. Firstly, the reflectance spectra of leaves in different seasons and regions were analyzed. Then, to solve the problem that 0-element in sparse random (SR) coding matrices affects the classification performance of error-correcting output codes (ECOC), two versions of supervision-mechanism-based ECOC algorithms, namely SM-ECOC-V1 and SM-ECOC-V2, were proposed in this paper. In addition,

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Othman, Kamal, and Ahmad Rad. "An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC." Applied Sciences 9, no. 3 (2019): 470. http://dx.doi.org/10.3390/app9030470.

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The ability to classify rooms in a home is one of many attributes that are desired for social robots. In this paper, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures, i.e., VGG16, VGG19, & Inception V3. The main objective is to recognize five indoor classes (bathroom, bedroom, dining room, kitchen, and living room) from a Places dataset. We considered 11600 images per class and subsequently fine-tuned the networks. The simulation studies suggest that cleaning the disparate data produced much better results in all the examined

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Lei, Lei, and Yafei Song. "Weighted Decoding for the Competence Reliability Problem of ECOC Multiclass Classification." Computational Intelligence and Neuroscience 2021 (October 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/5583031.

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Error-Correcting Output Codes has become a well-known, established technique for multiclass classification due to its simplicity and efficiency. Each binary split contains different original classes. A noncompetent classifier emerges when it classifies an instance whose real class does not belong to the metasubclasses which is used to learn the classifier. How to reduce the error caused by the noncompetent classifiers under diversity big enough is urgent for ECOC classification. The weighted decoding strategy can be used to reduce the error caused by the noncompetence contradiction through rel

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MORGAN, JOSEPH T., JISOO HAM, MELBA M. CRAWFORD, ALEX HENNEGUELLE, and JOYDEEP GHOSH. "ADAPTIVE FEATURE SPACES FOR LAND COVER CLASSIFICATION WITH LIMITED GROUND TRUTH DATA." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (2004): 777–99. http://dx.doi.org/10.1142/s0218001404003411.

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Classification of land cover based on hyperspectral data is very challenging because typically tens of classes with uneven priors are involved, the inputs are high dimensional, and there is often scarcity of labeled data. Several researchers have observed that it is often preferable to decompose a multiclass problem into multiple two-class problems, solve each such subproblem using a suitable binary classifier, and then combine the outputs of this collection of classifiers in a suitable manner to obtain the answer to the original multiclass problem. This approach is taken by the popular error

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WINDEATT, T., and G. ARDESHIR. "DECISION TREE SIMPLIFICATION FOR CLASSIFIER ENSEMBLES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (2004): 749–76. http://dx.doi.org/10.1142/s021800140400340x.

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The goal of designing an ensemble of simple classifiers is to improve the accuracy of a recognition system. However, the performance of ensemble methods is problem-dependent and the classifier learning algorithm has an important influence on ensemble performance. In particular, base classifiers that are too complex may result in overfitting. In this paper, the performance of Bagging, Boosting and Error-Correcting Output Code (ECOC) is compared for five decision tree pruning methods. A description is given for each of the pruning methods and the ensemble techniques. AdaBoost.OC which is a combi

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Mishra, Puneet, Alison Nordon, Julius Tschannerl, Guoping Lian, Sally Redfern, and Stephen Marshall. "Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products." Journal of Food Engineering 238 (June 13, 2018): 70–77. https://doi.org/10.1016/j.jfoodeng.2018.06.015.

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Tea is the most consumed manufactured drink in the world. In recent years, various high end analytical techniques such as high-performance liquid chromatography have been used to analyse tea products. However, these techniques require complex sample preparation, are time consuming, expensive and require a skilled analyst to carry out the experiments. Therefore, to support rapid and non-destructive assessment of tea products, the use of near infrared (NIR) (950-1760 nm) hyperspectral imaging (HSI) for classification of six different commercial tea products (oolong, green, yellow, white, black a

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Yu, Mian Shui, Yu Xie, and Xiao Meng Xie. "Age Classification Based on Feature Fusion." Applied Mechanics and Materials 519-520 (February 2014): 644–50. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.644.

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Age classification based on facial images is attracting wide attention with its broad application to human-computer interaction (HCI). Since human senescence is a tremendously complex process, age classification is still a highly challenging issue. In our study, Local Directional Pattern (LDP) and Gabor wavelet transform were used to extract global and local facial features, respectively, that were fused based on information fusion theory. The Principal Component Analysis (PCA) method was used for dimensionality reduction of the fused features, to obtain a lower-dimensional age characteristic

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Xie, Shu-Tong, Zong-Bao He, Qiong Chen, Rong-Xin Chen, Qing-Zhao Kong, and Cun-Ying Song. "Predicting Learning Behavior Using Log Data in Blended Teaching." Scientific Programming 2021 (August 20, 2021): 1–14. http://dx.doi.org/10.1155/2021/4327896.

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Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students’ learning behavior analysis and student performance prediction based on the data about students’ behavior logs in three consecutive years of blended teaching in a college’s “Java Language Programming” course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algor

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Mallary, C., C. J. Berg, John R. Buck, and Amit Tandon. "Detection and estimation of rainfall from broadband acoustic signals." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A97. http://dx.doi.org/10.1121/10.0018293.

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Jeff Nystuen contributed major advances in our understanding of the physical acoustics of rain falling on the ocean, culminating in his pioneering work quantifying rainfall from the shape of the acoustic spectrum. Ma and Nystuen [JAOT (2005)] exploited Vagle’s wind spectrum to self-calibrate hydrophones for long deployments as acoustic rain gauges. Their algorithm predominantly relied on 3 narrowband frequencies to detect rainfall, and correlated the rainfall amount with the power spectral density (PSD) at 5kHz. Recent research at UMass Dartmouth built upon the foundational work of Nystuen, Ma

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Han, Guo-Sheng, and Zu-Guo Yu. "ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites." Current Proteomics 16, no. 5 (2019): 359–65. http://dx.doi.org/10.2174/1570164616666190103143945.

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Background: The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem. Observation: Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular

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Samat, Alim, Naoto Yokoya, Peijun Du, et al. "Direct, ECOC, ND and END Frameworks—Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan." Remote Sensing 11, no. 16 (2019): 1953. http://dx.doi.org/10.3390/rs11161953.

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To facilitate the advances in Sentinel-2A products for land cover from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery, Sentinel-2A MultiSpectral Instrument Level-1C (MSIL1C) images are investigated for large-scale vegetation mapping in an arid land environment that is located in the Ili River delta, Kazakhstan. For accurate classification purposes, multi-resolution segmentation (MRS) based extended object-guided morphological profiles (EOMPs) are proposed and then compared with conventional morphological profiles (MPs), MPs with partial reconstruction (MPPR), object-

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Mishra, Partha, Shubhasish Sarkar, Sandip Saha Chowdhury, and Santanu Das. "Machine learning based stator-winding fault severity detection in induction motors." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 182. https://doi.org/10.11591/ijeecs.v38.i1.pp182-192.

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Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruct

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Partha, Mishra Shubhasish Sarkar Sandip Saha Chowdhury Santanu Das. "Machine learning based stator-winding fault severity detection in induction motors." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 182–92. https://doi.org/10.11591/ijeecs.v38.i1.pp182-192.

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Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruct

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Wiharto, K. usnanto Hari, and Herianto. "Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases." International Journal on Computational Science & Applications (IJCSA) 5, October (2015): 27–38. https://doi.org/10.5281/zenodo.3250715.

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Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis. Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4 types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be analyzed with multiclass classification approach. One of multiclass classification approach used, one of which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification supp

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Norah Abdullah Al-johani and Lamiaa A. Elrefaei. "Palmprint And Dorsal Hand Vein Multi-Modal Biometric Fusion Using Deep Learning." International Journal of Artificial Intelligence and Machine Learning 10, no. 2 (2020): 18–42. http://dx.doi.org/10.4018/ijaiml.2020070102.

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Advancements in biometrics have attained relatively high recognition rates. However, the need for a biometric system that is reliable, robust, and convenient remains. Systems that use palmprints (PP) for verification have a number of benefits including stable line features, reduced distortion and simple self-positioning. Dorsal hand veins (DHVs) are distinctive for every person, such that even identical twins have different DHVs. DHVs appear to maintain stability over time. In the past, different features algorithms were used to implement palmprint (PP) and dorsal hand vein (DHV) systems. Prev

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Zhang, Jie, and Kexin Zhou. "Identification of Solid and Liquid Materials Using Acoustic Signals and Frequency-Graph Features." Entropy 25, no. 8 (2023): 1170. http://dx.doi.org/10.3390/e25081170.

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Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can id

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Ahmed, Hosameldin O. A., Yuexiao Yu, Qinghua Wang, Mohamed Darwish, and Asoke K. Nandi. "Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission." Sensors 22, no. 1 (2022): 362. http://dx.doi.org/10.3390/s22010362.

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Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions. In addition, very little attention has been paid to the importance of developing fast and accurate techniques for the real-life application of open-circuit failures of IGBT

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Zhao, Mengjie, Kai Zhang, Guodong Chen, et al. "A Classification-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Production Optimization under Geological Uncertainty." SPE Journal 25, no. 05 (2020): 2450–69. http://dx.doi.org/10.2118/201229-pa.

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Summary Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization under geological uncertainty that maximizes the expectation of net present value (NPV) over all possible uncertainty models and minimizes the variance simultaneously. However, the optimization process involves a large number of decision variables, and the problem is computationally expensive. Surrogate-assisted evolutionary algorithms (SAEAs), which have proved to be an effective way to solve expensive problems, design computationally inexpensive functions to approximate each objective function. On

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Erişen, Serdar. "Real-Time Learning and Monitoring System in Fighting against SARS-CoV-2 in a Private Indoor Environment." Sensors 22, no. 18 (2022): 7001. http://dx.doi.org/10.3390/s22187001.

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The SARS-CoV-2 virus has posed formidable challenges that must be tackled through scientific and technological investigations on each environmental scale. This research aims to learn and report about the current state of user activities, in real-time, in a specially designed private indoor environment with sensors in infection transmission control of SARS-CoV-2. Thus, a real-time learning system that evolves and updates with each incoming piece of data from the environment is developed to predict user activities categorized for remote monitoring. Accordingly, various experiments are conducted

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Yan, Z., and Y. Yang. "Application of ECOC SVMS in Remote Sensing Image Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 191–96. http://dx.doi.org/10.5194/isprsarchives-xl-2-191-2014.

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Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs

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Thomas, Miracle. "Abstract LB118: Machine learning-based tumor grading in pancreatic ductal adenocarcinoma: Exploring texture features for automated classification and clinical decision support." Cancer Research 85, no. 8_Supplement_2 (2025): LB118. https://doi.org/10.1158/1538-7445.am2025-lb118.

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Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy and a leading cause of cancer-related death in the U.S. Due to late-onset symptoms, it often remains undiagnosed until advanced stages, resulting in poor prognosis. This study presents a machine learning approach to classify tumor grades based on texture features extracted from histological images, offering insights into prognosis and treatment decisions. A 2019 study by Qiu et al. demonstrated the effectiveness of machine learning-based CT texture analysis in predicting PDAC histopathological grades, achieving 86% ac

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Li, Nan, Jiafei Wu, Lili Shan, and Luan Yi. "Transient Stability Assessment of Power Systems Based on CLV-GAN and I-ECOC." Energies 17, no. 10 (2024): 2278. http://dx.doi.org/10.3390/en17102278.

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In order to improve the multi-class assessment performance of transient stability in power systems, a multi-class assessment model that combines the CLV-GAN algorithm with an improved error-correcting output coding technique is proposed in the paper. To address the issue of the small number of unstable samples in power systems, a sample generation model is constructed by combining a dual-encoder VAE with a GAN network. The model generates effective artificial samples to balance the sample ratio between categories by learning the latent distribution of aperiodic and oscillatory unstable samples

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PIMENTA, EDGAR, JOÃO GAMA, and ANDRÉ CARVALHO. "THE DIMENSION OF ECOCs FOR MULTICLASS CLASSIFICATION PROBLEMS." International Journal on Artificial Intelligence Tools 17, no. 03 (2008): 433–47. http://dx.doi.org/10.1142/s0218213008003984.

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Several classification problems involve more than two classes. These problems are known as multiclass classification problems. One of the approaches to deal with multiclass problems is their decomposition into a set of binary problems. Recent work shows important advantages related with this approach. Several strategies have been proposed for this decomposition. The strategies most frequently used are All-vs-All, One-vs-All and Error Correction Output Codes (ECOC). ECOCs are based on binary words (codewords) and have been adapted to deal with multiclass problems. For such, they must comply wit

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Chen, Yi Ming, and Yue Hui Chen. "Predict the Tertiary Structure of Protein with Error-Correcting Output Coding and Flexible Neural Tree." Advanced Materials Research 756-759 (September 2013): 3781–84. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3781.

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In this paper we intend to apply a new method to predict tertiary structure. A novel hybrid feature adopted is composed of physicochemical composition (PCC), recurrence quantification analysis (RQA) and pseudo amino acid composition (PseAA). We use the Error Correcting Output Coding (ECOC) based on three flexible neural tree models as the classifiers. 640 dataset is selected to our experiment. The predict accuracy with our method on this data set is 60.23%, higher than some other methods on the 640 datasets. So, our method is feasible and effective in some extent.

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Lachaize, Marie, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, and Roger Reynaud. "Evidential framework for Error Correcting Output Code classification." Engineering Applications of Artificial Intelligence 73 (August 2018): 10–21. http://dx.doi.org/10.1016/j.engappai.2018.04.019.

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Han, Lei, and Xia Hua. "RFID Backscatter Based Sport Motion Sensing Using ECOC-Based SVM." Sensors 23, no. 17 (2023): 7324. http://dx.doi.org/10.3390/s23177324.

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With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based s

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XIN, Yi, Gong-de GUO, Li-fei CHEN, and Jie HUANG. "Output code algorithm for ierarchical error correcting based on KNNModel." Journal of Computer Applications 29, no. 11 (2009): 3051–55. http://dx.doi.org/10.3724/sp.j.1087.2009.03051.

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Chen, Xi, Weihua Cao, Chao Gan, Wenkai Hu, and Min Wu. "A Hybrid Reducing Error Correcting Output Code for Lithology Identification." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 10 (2020): 2254–58. http://dx.doi.org/10.1109/tcsii.2019.2950269.

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Poladi, Irfan, and Hitesh Ishwardas. "Review paper on Error Correcting Output Code Based on Multiclass Classification." International Journal of Scientific Research 2, no. 2 (2012): 134–36. http://dx.doi.org/10.15373/22778179/feb2013/45.

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Sharma, Puneet. "Dihedral Group D4—A New Feature Extraction Algorithm." Symmetry 12, no. 4 (2020): 548. http://dx.doi.org/10.3390/sym12040548.

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In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Orie

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Lai, Chi Qin, Haidi Ibrahim, Aini Ismafairus Abd Hamid, and Jafri Malin Abdullah. "Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM." Sensors 20, no. 18 (2020): 5234. http://dx.doi.org/10.3390/s20185234.

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Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility

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Sharma, Sangat, Suresh Basnet, and Raju Khanal. "Implementation of Error Correction on IBM Quantum Computing Devices." Journal of Nepal Physical Society 8, no. 1 (2022): 7–15. http://dx.doi.org/10.3126/jnphyssoc.v8i1.48278.

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Quantum noise cannot be avoided in the quantum computing devices due to unstable nature of qubits and signals. The error caused by quantum noise can be detected and corrected using different error correcting codes. In this work, we have tested the feasibility and accuracy of three qubit bit flip and phase flip error correcting code in quantum computer provided by International Business Machine Quantum Experience (IBM QX) cloud platform. Among five quantum processors, ibmq_ourense is found to have highest average accuracy 77.9% ± 3.09% on all qubits simultaneously. Three qubits bit flip error c

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Konstantinidis, Stavros, Nelma Moreira, and Rogério Reis. "Randomized generation of error control codes with automata and transducers." RAIRO - Theoretical Informatics and Applications 52, no. 2-3-4 (2018): 169–84. http://dx.doi.org/10.1051/ita/2018015.

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We introduce the concept of an -maximal error-detecting block code, for some parameter in (0,1), in order to formalize the situation where a block code is close to maximal with respect to being error-detecting. Our motivation for this is that it is computationally hard to decide whether an error-detecting block code is maximal. We present an output-polynomial time randomized algorithm that takes as input two positive integers N, ℓ and a specification of the errors permitted in some application, and generates an error-detecting, or error-correcting, block code of length ℓ that is 99%-maximal, o

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