Relevant bibliographies by topics / Error Correcting Output Code (ECOC)

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  2. Dissertations / Theses
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Academic literature on the topic 'Error Correcting Output Code (ECOC)'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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

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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 matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.
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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-versus- all, dense-random, sparserandom, DECOC, forest- ECOC, and ECOC-ONE) and decoding designs (hamming, Euclidean, inverse hamming, laplacian, β-density, density, attenuated, lossbased, probabilistic kernel-based, and loss weighted) with the framework defined by the authors, as well as the option to include your own coding, decoding, and base classifier
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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 paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.
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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 considered as two successive layers of the network, while the incremental SVMs can be viewed as weighted links between two successive layers. In the pre-training procedure, supervisory information, i.e., class labels, can be used during the network initialization. The incremental SVMs lead this procedure to be very efficient, especially for large-scale applications. We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results demonstrate that DeepECOCs perform, not only better than existing ECOC and feature learning algorithms, but also related to deep learning ones in most cases.
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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 function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.
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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>&nbsp;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.&nbsp;<strong>Method:</strong>&nbsp;A long-duration ECG categorization algorithm named AFECOC is proposed. For this one-minute-long 71 signals are attained from the Physionet&rsquo;s &ldquo;MIT-BIH arrhythmia (MA)&rdquo; and &ldquo;AF&rdquo; database. Two-stage filtering of noisy signals is employed before signal analysis. Four algorithms i.e. Error-Correcting Output Code (ECOC), Na&iuml;ve Bayes, Decision Tree, and K-Nearest Neighbor(K-NN) are applied to reduce the feature set and then signals are classified with ECOC classifier.&nbsp;<strong>Findings:</strong>&nbsp;It was found that the ECOC algorithm gives the highest accuracy of 81.95% on the complete feature set. To exclude the irrelevant features, the highest performing algorithm ECOC was used that extracts the combination of the feature sets that get most affected during AF. The combination of &rsquo;heart-beat&rsquo; and &rsquo;mean QT-interval&rsquo; are found to be the most relevant features affected during AF events. The accuracy of these two features was evaluated with four classifiers namely ECOC, Na&iuml;ve Bayes, Decision tree-based and K mean classifier and the accuracy obtained was 89.6%, 76.19%, 76.19%, and 61% respectively. It concludes that the proposed methodology achieved the highest accuracy of 89.6% with the ECOC classifier. Finally, all the AF rhythms have been checked using annotated labels for spontaneous change in QT-interval to verify the designed methodologies.&nbsp;<strong>Novelty:</strong>&nbsp;Instead of missing P-waves and RR-interval variation, recognition of mean QT interval variation-based AF event detection algorithm gives better accuracy for longer signals. Hence, it can be implemented in Real- Time continuous monitoring. <strong>Keywords:</strong> Atrial Fibrillation (AF); Classification; Error Correcting Output Code (ECOC); Feature extraction; QTinterval
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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 architectures, like the ECOC-based one, and by applying two existing attacks. In contrast to previous findings, our analysis reveals that ECOC-based networks can be attacked quite easily by introducing a small adversarial perturbation. Moreover, the adversarial examples can be generated in such a way to achieve high probabilities for the predicted target class, hence making it difficult to use the prediction confidence to detect them. Our findings are proven by means of experimental results obtained on MNIST, CIFAR-10, and GTSRB classification tasks.
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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 Scaled Invariant Feature Transformation (SIFT) based features in classification. The best accuracy (62.4%) is obtained with ternary complete ECOC coding design andk-NN classifier (standardized Euclidean distance measure and inverse weighting). The best result is comparable with our earlier research. The quality identification of hiPSC colony images is an essential problem to be solved before hiPSCs can be used in practice in large-scale. ECOC methods examined are promising techniques for solving this challenging problem.
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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 called as Reject-Option-based Re-encoding ECOC (ROECOC). The cost-sensitive classification model and cost-loss function based on Receiver Operating Characteristic (ROC) curve are built respectively. The optimal reject threshold values are obtained by combing the condition to be met for minimizing the loss function and the ROC convex hull. In so doing, reject option (t1, t2) provides a three-symbol output to make dichotomizers more competent and ROECOC more universal and practical for cost-sensitive classification issue. Experimental results on two kinds of datasets show that our scheme with low-degree freedom of initialized ECOC can effectively enhance accuracy and reduce cost.
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Dissertations / Theses on the topic "Error Correcting Output Code (ECOC)"

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Escalera, Guerrero Sergio. "Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2008. http://hdl.handle.net/10803/5789.

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Molts problemes de la vida quotidiana estan plens de decisions multi-classe. En l'àmbit del Reconeixement de Patrons, s'han proposat moltes tècniques d'aprenentatge que treballen sobre problemes de dos classes. No obstant, la extensió de classificadors binaris al cas multi-classe és una tasca complexa. En aquest sentit, Error-Correcting Output Codes (ECOC) han demostrat ser una eina potent per combinar qualsevol nombre de classificadors binaris i així modelar problemes multi-classe. No obstant, encara hi ha molts punts oberts sobre les capacitats del framework d'ECOC. En aquesta tesis, els dos estats principals d'un disseny ECOC són analitzats: la codificació i la decodificació. Es presenten diferents alternatives de dissenys dependents del domini del problema. Aquests dissenys fan ús del coneixement del domini del problema per minimitzar el nombre de classificadors que permeten obtenir un alt rendiment de classificació. Per altra banda, l'anàlisi de la codificació de dissenys d'ECOC es emprada per definir noves regles de decodificació que prenen total avantatja de la informació provinent del pas de la codificació. A més a més, com que classificacions exitoses requereixen rics conjunts de característiques, noves tècniques de detecció/extracció de característiques es presenten i s'avaluen en els nous dissenys d'ECOC. L'avaluació de la nova metodologia es fa sobre diferents bases de dades reals i sintètiques: UCI Machine Learning Repositori, símbols manuscrits, senyals de trànsit provinents de sistemes Mobile Mapping, imatges coronàries d'ultrasò, imatges de la Caltech Repositori i bases de dades de malats de Chagas. Els resultats que es mostren en aquesta tesis mostren que s'obtenen millores de rendiment rellevants tant a la codificació com a la decodificació dels dissenys d'ECOC quan les noves regles són aplicades.<br>Many real problems require multi-class decisions. In the Pattern Recognition field, many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga's disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account.
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Bade, Peter. "A 1Mbps 0.18μm CMOS Soft-output Decoder for Product Turbo Codes". Thesis, 2009. http://hdl.handle.net/1807/17493.

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A product turbo code (PTC) decoder application specific integrated circuit (ASIC) is designed in 0.18μm 1P6M CMOS with embedded SRAM. From simulation, an operating frequency of 73.1 MHz at typical conditions is obtained, yielding a throughput of 3.8 Mbps with 4 decoding iterations, while consuming 103.4 mW. The total area is 5.13 mm2. Assuming the ASIC would be used as a hard macro, the area could be reduced to 1.7 mm2. The ASIC was tested at 20 MHz under typical conditions, which resulted in a throughput of 1.0 Mbps at 1.8V supply while consuming 36.6 mW. By making a slight modification, this design can be easily scaled to support IEEE 802.16d WiMAX. Allow for this, and moving to a 45nm process an estimated throughput of 9.44 Mbps with 4 iterations can be obtained. Total macro area would be approximately 0.11 mm2.
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Book chapters on the topic "Error Correcting Output Code (ECOC)"

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Smith, R. S., and T. Windeatt. "Decoding Rules for Error Correcting Output Code Ensembles." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11494683_6.

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Ghaderi, Reza, and Terry Windeatt. "Least Squares and Estimation Measures via Error Correcting Output Code." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_15.

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Smith, R. S., and T. Windeatt. "Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12127-2_19.

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Smith, Raymond S., and Terry Windeatt. "The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02326-2_1.

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Kumar, Dhruba. "Performance Assessment and Improvement of Classifiers Using Error Correcting Output Code for Islanding Detection in Microgrid." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4971-5_45.

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"Error-Correcting Output Codes (ECOC)." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_100141.

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Hj Wan Yussof, Wan Nural Jawahir, Nurfarahim Shaharudin, Muhammad Suzuri Hitam, Ezmahamrul Afreen Awalludin, Mohd Uzair Rusli, and Daphne Z. Hoh. "Photo Identification of Sea Turtles Using AlexNet and Multi-Class SVM." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200549.

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Up to now, identification of sea turtle species mainly for tracking the population usually relied on flipper tags or through other physical markers. However, this approach is not practical due to the missing tags over some period. Due to this matter, we propose a photo identification system of the individual sea turtle based on the convolutional neural network (CNN) using a pre-trained AlexNet CNN and error-correcting output codes (ECOC) SVM. Experiments were performed on 300 images obtained from Biodiversity Research Center, Academia Sinica, Taiwan. Using Alexnet and ECOC SVM, the overall accuracy achieved is 62.9%. The results indicate that features obtained from the CNN are capable of identifying photo of sea turtles.
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Conference papers on the topic "Error Correcting Output Code (ECOC)"

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Li, Ao, and Xi Chen. "An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661547.

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Nagane, Aniket S., Chandrashekhar H. Patil, and Shankar M. Mali. "Classification of Brahmi script characters using HOG features and multiclass error-correcting output codes (ECOC) model containing SVM binary learners." In 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2023. http://dx.doi.org/10.1109/iitcee57236.2023.10091084.

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Kodali, Anuradha, William Donat, Satnam Singh, Kihoon Choi, and Krishna Pattipati. "Dynamic Fusion and Parameter Optimization of Multiple Classifier Systems." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51274.

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We propose a fusion architecture that combines a set of classifier decisions over a time window to isolate dynamically evolving faults in gas turbine engines. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple classifiers over time. The resulting problem is solved via a primal-dual optimization framework. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC) and thus solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window dynamic fusion method that exploits temporal data correlations over time. The window size provides a trade-off between diagnostic errors and decision delays. The third step optimizes the fusion parameters using a genetic algorithm. The probability of detection and false alarm probability of each classifier are the fusion parameters; these probabilities are jointly optimized as part of the fusion architecture instead of optimizing the parameters of each classifier separately. The proposed algorithm is demonstrated by computing the diagnostic performance metrics on a twin-spool commercial jet engine data.
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AbdElrahman, Shaza Merghani, and Ajith Abraham. "Intrusion detection using error correcting output code based ensemble." In 2014 14th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2014. http://dx.doi.org/10.1109/his.2014.7086194.

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Sun, Yijun, Sinisa Todorovic, Jian Li, and Dapeng Wu. "Unifying the error-correcting and output-code AdaBoost within the margin framework." In the 22nd international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102461.

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Ho, Shen-Shyang, Mathew Marchiano, Scott Zockoll, and Hieu Nguyen. "An Error-Correcting Output Code Framework for Lifelong Learning without a Teacher." In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2020. http://dx.doi.org/10.1109/ictai50040.2020.00048.

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Naga, Atsushi, Haruki Kawanaka, Md Shoaib Bhuiyan, and Koji Oguri. "Multi-class identification of driver's cognitive distraction with error-correcting output coding (ECOC) method." In 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2009. http://dx.doi.org/10.1109/itsc.2009.5309706.

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Songsiri, Patoomsiri, Thimaporn Phetkaew, Ryutaro Ichise, and Boonserm Kijsirikul. "Sub-classifier construction for error correcting output code using minimum weight perfect matching." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889436.

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Jiao, Yang, Shahram Latifi, and Mei Yang. "Self Error Detection and Correction for Noisy Labels Based on Error Correcting Output Code in Convolutional Neural Networks." In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2019. http://dx.doi.org/10.1109/ccwc.2019.8666460.

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Smith, Raymond Stuart, and Terry Windeatt. "A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.24.

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