Classification with reject option

Herbei, Radu; Wegkamp, Marten

doi:10.1002/cjs.5550340410

Cited by 181 publications

(210 citation statements)

References 16 publications

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“…Several heuristic approaches were proposed to deal with these problems [98,99,100] or to treat it as a probabilistic approach [101,102]. In LVQ systems usually reject options are applied after model training [103,104].…”

Section: Reject or Classify -Secure Classificationmentioning

confidence: 99%

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Villmann

Bohnsack

Kaden

2016

Journal of Artificial Intelligence and Soft Computing Research

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Learning vector quantization (LVQ) is one of the most powerful approaches for prototype based classification of vector data, intuitively introduced by Kohonen. The prototype adaptation scheme relies on its attraction and repulsion during the learning providing an easy geometric interpretability of the learning as well as of the classification decision scheme. Although deep learning architectures and support vector classifiers frequently achieve comparable or even better results, LVQ models are smart alternatives with low complexity and computational costs making them attractive for many industrial applications like intelligent sensor systems or advanced driver assistance systems.Nowadays, the mathematical theory developed for LVQ delivers sufficient justification of the algorithm making it an appealing alternative to other approaches like support vector machines and deep learning techniques.This review article reports current developments and extensions of LVQ starting from the generalized LVQ (GLVQ), which is known as the most powerful cost function based realization of the original LVQ. The cost function minimized in GLVQ is an soft-approximation of the standard classification error allowing gradient descent learning techniques. The GLVQ variants considered in this contribution, cover many aspects like bordersensitive learning, application of non-Euclidean metrics like kernel distances or divergences, relevance learning as well as optimization of advanced statistical classification quality measures beyond the accuracy including sensitivity and specificity or area under the ROC-curve.According to these topics, the paper highlights the basic motivation for these variants and extensions together with the mathematical prerequisites and treatments for integration into the standard GLVQ scheme and compares them to other machine learning approaches. For detailed description and mathematical theory behind all, the reader is referred to the respective original articles.Thus, the intention of the paper is to provide a comprehensive overview of the stateof-the-art serving as a starting point to search for an appropriate LVQ variant in case of a given specific classification problem as well as a reference to recently developed variants and improvements of the basic GLVQ scheme.

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Section: Reject or Classify -Secure Classificationmentioning

confidence: 99%

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Villmann

Bohnsack

Kaden

2016

Journal of Artificial Intelligence and Soft Computing Research

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show abstract

“…Reliability scoring approaches considered in this paper provide continuous estimates and allow for reliability ranking of predicted compounds. Such ranking can be explored by prediction with a reject option, 20 where analogous to the usage of the AD in QSAR, the prediction algorithm has the opportunity to decline to predict the response of an example if it is unreliable or if the reliability falls outside of some user-defined threshold. Predictors with reject options have been extensively investigated within classification, 20 but because of the absence of estimates analogous to class probabilities, the reject option is much less studied in regression.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models

Toplak

Močnik

Polajnar

et al. 2014

J. Chem. Inf. Model.

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The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package (https:// bitbucket.org/biolab/orange-reliability) to the Orange data mining suite.

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“…For example, Herbei and Wegkamp (2006) consider classifiers that render three possible outputs: 0, 1 and R. The option R expresses doubt and is used to distinguish observations that are hard to classify in an automatic way. The possibility of taking no decision ("I do not know") is of great importance in practice, for instance, in case of medical diagnoses.…”

Section: Comparison: Risk Of Erroneous Predictionsmentioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

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Classification with reject option

Cited by 181 publications

References 16 publications

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models

Robust ordinal regression in preference learning and ranking

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