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On the Calibration of Multiclass Classification with Rejection
[article]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2010.11748v4 fatcat:j3okg56jarbjzilu4gz7sq6wdaOther Versions
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
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
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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
2016
Machine Learning
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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
2001
Proceedings of the National Academy of Sciences of the United States of America
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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
2005
Machine Learning
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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
2021
Frontiers in Neuroscience
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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]
2008
arXiv
pre-print
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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
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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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]
2010
arXiv
pre-print
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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
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
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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]
2009
Lecture Notes in Computer Science
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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
2007
2007 IEEE Conference on Computer Vision and Pattern Recognition
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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
2016
Computational and Mathematical Methods in Medicine
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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
2016
IEEE transactions on fuzzy systems
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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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