A confidence value estimation method for handwritten Kanji character recognition and its application to candidate reduction

Ishidera, Eiki; Nishiwaki, Daisuke; Sato, Atsushi

doi:10.1007/s10032-003-0118-8

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…The reliable estimation of GMMs is particularly problematic for high dimensional data. Therefore, Ishidera et al [30] propose a suitable approximation of the probability density function for high dimensionality, which is based on a low dimensional projection of the data.…”

Section: Related Workmentioning

confidence: 99%

Optimum Reject Options for Prototype-based Classification

Fischer,

Hammer,

Wersing

2015

Preprint

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We analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts. We compare reject schemes with global thresholds, and local thresholds for the Voronoi cells of the classifier. For the latter, we develop a polynomial-time algorithm to compute optimum thresholds based on a dynamic programming scheme, and we propose an intuitive linear time, memory efficient approximation thereof with competitive accuracy. Evaluating the performance in various benchmarks, we conclude that local reject options are beneficial in particular for simple prototype-based classifiers, while the improvement is less pronounced for advanced models. For the latter, an accuracy-reject curve which is comparable to support vector machine classifiers with state of the art reject options can be reached.

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Section: Related Workmentioning

confidence: 99%

Optimum Reject Options for Prototype-based Classification

Fischer,

Hammer,

Wersing

2015

Preprint

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“…However, the accuracy of the estimation significantly influences the performance of reject classification. Other methods, such as estimating the data distribution [4], probability density function [9], and confidence interval [5], are proposed for classification with rejection.…”

Section: Introductionmentioning

confidence: 99%

Abstaining Classification When Error Costs are Unequal and Unknown

Guan,

Zhang,

Cheng

et al. 2018

Preprint

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Abstaining classification aims to reject to classify the easily misclassified examples, so it is an effective approach to increase the classification reliability and reduce the misclassification risk in the costsensitive applications. In such applications, different types of errors (false positive or false negative) usually have unequal costs. And the error costs, which depend on specific applications, are usually unknown. However, current abstaining classification methods either do not distinguish the error types, or they need the cost information of misclassification and rejection, which are realized in the framework of cost-sensitive learning. In this paper, we propose a bounded-abstention method with two constraints of reject rates (BA2), which performs abstaining classification when error costs are unequal and unknown. BA2 aims to obtain the optimal area under the ROC curve (AUC) by constraining the reject rates of the positive and negative classes respectively. Specifically, we construct the receiver operating characteristic (ROC) curve, and stepwise search the optimal reject thresholds from both ends of the curve, until the two constraints are satisfied. Experimental results show that BA2 obtains higher AUC and lower total cost than the state-of-the-art abstaining classification methods. Meanwhile, BA2 achieves controllable reject rates of the positive and negative classes.

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“…Measures based on probabilities often either require a probabilistic classification model [5,36] or a probabilistic model on top of the trained classifier to estimate the probabilities [11,29]. Both approaches are computationally expensive.…”

Section: Certainty Measuresmentioning

confidence: 99%

“…Common approaches for rejection usually rely on an estimation of class probabilities on top of a classifier to enable an optimum rejection following the approaches [5,6], see e. g. [11,29,30,7,8].…”

Section: Introductionmentioning

confidence: 99%

Efficient rejection strategies for prototype-based classification

Fischer

Hammer

Wersing³

2015

Neurocomputing

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Due to intuitive training algorithms and model representation, prototypebased models are popular in settings where on-line learning and model interpretability play a major role. In such cases, a crucial property of a classifier is not only which class to predict, but also if a reliable decision is possible in the first place, or whether it is better to reject a decision. While strong theoretical results for optimum reject options in the case of known probability distributions or estimations thereof are available, there do not exist wellaccepted reject strategies for deterministic prototype-based classifiers. In this contribution, we present simple and efficient reject options for prototype-based classification, and we evaluate their performance on artificial and benchmark data sets using the example of learning vector quantization. We demonstrate that the proposed reject options improve the accuracy in most cases, and their performance is comparable to an optimal reject option of the Bayes classifier in cases where the latter is available. Further, we show that the results are comparable to a well established reject option for support vector machines in cases where learning vector quantization classifiers are suitable for the given classification task, even providing better results in some cases.

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A confidence value estimation method for handwritten Kanji character recognition and its application to candidate reduction

Cited by 5 publications

References 10 publications

Optimum Reject Options for Prototype-based Classification

Optimum Reject Options for Prototype-based Classification

Abstaining Classification When Error Costs are Unequal and Unknown

Efficient rejection strategies for prototype-based classification

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