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On the Calibration of Multiclass Classification with Rejection [article]

Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
2019 arXiv   pre-print
We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification.  ...  First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case.  ...  NC was supported by MEXT scholarship and JST AIP challenge. JH was supported by KAKENHI 18K17998.  ... 
arXiv:1901.10655v2 fatcat:ubruodniyveejeql23g2lg7oba

Classification with Rejection Based on Cost-sensitive Classification [article]

Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, Masashi Sugiyama
2021 arXiv   pre-print
In this paper, based on the relationship between classification with rejection and cost-sensitive classification, we propose a novel method of classification with rejection by learning an ensemble of cost-sensitive  ...  The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection.  ...  of Tokyo, for providing us the Reedbush supercomputer system to conduct the experiments.  ... 
arXiv:2010.11748v5 fatcat:ucvjzeeubjdsdfcmqkfausnm54

Detection of text quality flaws as a one-class classification problem

Maik Anderka, Benno Stein, Nedim Lipka
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
We argue that common binary or multiclass classification approaches are ineffective in here, and we underpin our approach by a real-world application: we employ a dedicated one-class learning approach  ...  In particular, we propose to cast the detection of text quality flaws as a one-class classification problem: we are given only positive examples (= texts containing a particular quality flaw) and decide  ...  For an in-depth discussion of one-class classification and a survey of respective methodologies see [14, 9] .  ... 
doi:10.1145/2063576.2063954 dblp:conf/cikm/AnderkaSL11 fatcat:2kyx2o6lmvcmlgiy7boscp6yt4

Nearest neighbors distance ratio open-set classifier

Pedro R. Mendes Júnior, Roberto M. de Souza, Rafael de O. Werneck, Bernardo V. Stein, Daniel V. Pazinato, Waldir R. de Almeida, Otávio A. B. Penatti, Ricardo da S. Torres, Anderson Rocha
2016 Machine Learning  
Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems.  ...  For validation, we consider large freely-available benchmarks with different openset recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature  ...  Acknowledgements Part of the results presented in this paper were obtained through the project "Pattern recognition and classification by feature engineering, *-fusion, open-set recognition, and meta-recognition  ... 
doi:10.1007/s10994-016-5610-8 fatcat:bswjzmxrfjcbbfixqa5jq25bvy

Multiclass cancer diagnosis using tumor gene expression signatures

S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J. P. Mesirov, T. Poggio, W. Gerald (+3 others)
2001 Proceedings of the National Academy of Sciences of the United States of America  
The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data.  ...  The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm.  ...  We thank David Waltregny for initial review of pathology, Christine Huard and Michelle Gaasenbeek for expert technical assistance, and Leslie Gaffney for insightful editorial review.  ... 
doi:10.1073/pnas.211566398 pmid:11742071 pmcid:PMC64998 fatcat:gpfrbebbkrdx5ossdxmsv5xnv4

Online Multiclass Learning with k-Way Limited Feedback and an Application to Utterance Classification

Hiyan Alshawi
2005 Machine Learning  
This paper introduces a setting for multiclass online learning with limited feedback and its application to utterance classification.  ...  We present experiments on the effect of varying k and the weight update algorithms on the learning curve for online utterance classification.  ...  There are variations on the basic setting such as the binary classification case in which the input belongs to one of two output classes, the multiclass case in which it belongs to exactly one of K possible  ... 
doi:10.1007/s10994-005-0914-0 fatcat:gwczsgn5szgprbqbemqkdaqu2q

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Baoguo Xu, Dalin Zhang, Yong Wang, Leying Deng, Xin Wang, Changcheng Wu, Aiguo Song
2021 Frontiers in Neuroscience  
functions of patients with motor disorders.  ...  Grasping is one of the most indispensable functions of humans.  ...  Both binary and multiclass classification were carried out on the preprocessed offline data.  ... 
doi:10.3389/fnins.2021.684547 pmid:34650398 pmcid:PMC8505714 fatcat:2mrxah2f6zd7re7l4xo6ceeoxq

Quantum classification [article]

Sébastien Gambs
2008 arXiv   pre-print
task, such as the weighted binary and the multiclass versions.  ...  Quantum classification is defined as the task of predicting the associated class of an unknown quantum state drawn from an ensemble of pure states given a finite number of copies of this state.  ...  Acknowledgments I would like to thanks Gilles Brassard for enlightening discussions on the subject and Frédéric Dupuis for proofreading an early version of this paper and his suggestions and comments.  ... 
arXiv:0809.0444v2 fatcat:uhapxahstbfmnbfthrympvtwly

The offset tree for learning with partial labels

Alina Beygelzimer, John Langford
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
In particular, it has regret at most (k − 1) times the regret of the binary classifier it uses, where k is the number of decisions, and no reduction to binary classification can do better.  ...  The algorithm is an optimal reduction from this problem to binary classification.  ...  Note that k-class classification can be reduced to binary classification with a regret guarantee that does not depend on k.  ... 
doi:10.1145/1557019.1557040 dblp:conf/kdd/BeygelzimerL09 fatcat:3n27nfdjwbhljaebdspe6qjsre

Error-Correcting Tournaments [article]

Alina Beygelzimer, John Langford, Pradeep Ravikumar
2010 arXiv   pre-print
The results improve on the PECOC construction SECOC with an exponential improvement in computation, from O(k) to O(_2 k), and the removal of a square root in the regret dependence, matching the best possible  ...  We present a family of pairwise tournaments reducing k-class classification to binary classification. These reductions are provably robust against a constant fraction of binary errors.  ...  multiclass label, 1 if the right parent's output matches, and reject the example otherwise.  ... 
arXiv:0902.3176v4 fatcat:2jysxunlfne3dldzfukuxjreo4

Error limiting reductions between classification tasks

Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
then the cost-sensitive classifier has cost at most 2 times the expected sum of costs of all possible lables.  ...  We use this model to devise a new reduction from multi-class cost-sensitive classification to binary classification with the following guarantee: If the learned binary classifier has error rate at most  ...  ., 2003) algorithm is a (very simple) reduction from importance weighted classification to classification based on rejection sampling.  ... 
doi:10.1145/1102351.1102358 dblp:conf/icml/BeygelzimerDHLZ05 fatcat:pb2i5k7v4jaxrji4jj3wsgpkce

Error-Correcting Tournaments [chapter]

Alina Beygelzimer, John Langford, Pradeep Ravikumar
2009 Lecture Notes in Computer Science  
These reductions are provably robust against a constant fraction of binary errors, and match the best possible computation and regret up to a constant.  ...  We present a family of pairwise tournaments reducing k-class classification to binary classification.  ...  multiclass label, 1 if the right parent's output matches, and reject the example otherwise.  ... 
doi:10.1007/978-3-642-04414-4_22 fatcat:et3ish4wfvg7vphxwnu3k3sonm

Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network

Jingdan Zhang, Shaohua Kevin Zhou, Leonard McMillan, Dorin Comaniciu
2007 2007 IEEE Conference on Computer Vision and Pattern Recognition  
In addition, we only need one integral image/volume with no need of image/volume rotation.  ...  We implement PBN using a graph-structured network that alternates the two tasks of foreground/background discrimination and pose estimation for rejecting negatives as quickly as possible.  ...  The implemented structure enables fast rejection of patches both on the background and of wrong poses with the aid of only one integral volume/image; and (iv) We present in Section 5 a real-time algorithm  ... 
doi:10.1109/cvpr.2007.383275 dblp:conf/cvpr/ZhangZMC07 fatcat:j3ahhvx46ngc5feyiy2ipzrqgq

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

Henry Joutsijoki, Markus Haponen, Jyrki Rasku, Katriina Aalto-Setälä, Martti Juhola
2016 Computational and Mathematical Methods in Medicine  
In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based  ...  However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible  ...  Acknowledgments The first author is thankful for Ella and Georg Ehrnrooth Foundation and OskarÖflund Foundation for the support.  ... 
doi:10.1155/2016/3091039 pmid:27493680 pmcid:PMC4963598 fatcat:aq2iqky7uneijdba65uzqfwlgy

Ambiguity-Based Multiclass Active Learning

Ran Wang, Chi-Yin Chow, Sam Kwong
2016 IEEE transactions on fuzzy systems  
One solution to multiclass AL (MAL) is evaluating the informativeness of unlabeled samples by an uncertainty model, and selecting the most uncertain one for query.  ...  Index Terms-Active learning, ambiguity, fuzzy sets and fuzzy logic, possibility approach, multiclass.  ...  In the context of AL, if the memberships are possibilistic, then 1) µ i (x) = 0 means that class i is rejected as impossible for x; 2) µ i (x) = 1 means that class i is totally possible for x; 3) at least  ... 
doi:10.1109/tfuzz.2015.2451698 fatcat:ykxbvbtb25aqfjtnkwmokjq63m
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