Optimal local rejection for classifiers

Fischer, Lydia; Hammer, Barbara; Wersing, Heiko

doi:10.1016/j.neucom.2016.06.038

Cited by 33 publications

(16 citation statements)

References 37 publications

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“…8 if x has a label then 9 Run the SupervisedMode(x, s1) (Algorithm 5); 10 else 11 Run the UnsupervisedMode(x, s1) (Algorithm 4); 12 if nwins = age wins then 13 Remove nodes with winsj < lp × age wins; 14 Update the connections of the remaining nodes; 15 Reset the number of wins of the remaining nodes: In the organization phase, the network is initialized, and the nodes start to compete to form clusters of randomly chosen input patterns. The first node of the map is created at the same position of the first input pattern.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Recent SOM-based methods employ a threshold defining the minimum level of activation for an input pattern to be considered associated with a cluster prototype. This threshold level is a parameter of the model which is shared by all prototypes [2], [6], thus, the regions that a prototype can represent are not learned at all, or they are normally estimated using supervised approaches, as in [11].…”

Section: Introductionmentioning

confidence: 99%

“…Such rejection options define the first step towards an adaptation of the model complexity tailored to data regions with a high degree of uncertainty [14]. So far, most models that use rejection options deals with just a single threshold, as well as most of them can handle only with binary classification [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Braga

Bassani

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled ones. Hence, Semi-Supervised learning models can benefit from both types of data to improve the obtained performance. Also, it is important to develop methods that are easy to parameterize in a way that is robust to the different characteristics of the data at hand. This article presents a new method based on Self-Organizing Map (SOM) for clustering and classification, called Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of learning at training time, according to the availability of labels, as in previous models, and can automatically adjust itself to the local variance observed in each data cluster. The results show that the ALTSS-SOM surpass the performance of other semisupervised methods in terms of classification, and other pure clustering methods when there are no labels available, being also less sensitive than previous methods to the parameters values.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Braga

Bassani

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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“…(10). This way, all observations in the training set can be used to define the rejection threshold of a class, instead of its observations only (Fischer et al 2016). Such scheme provided better results in the performed experiments, what could be possibly related to the difference between open set recognition and classification with rejection-option: the first requires a global notion of the uncertainty with respect to ruling an observation as an element of a known class, while the second is focused on minimizing the cost resulting from misclassifications.…”

Section: Optimal Thresholdingmentioning

confidence: 99%

“…There is a rich variety of works in the literature regarding classification with reject option (Fischer et al 2016;Herbei and Wegkamp 2006;Bartlett and Wegkamp 2008;Yuan and Wegkamp 2010;Fumera and Roli 2002;Zhang and Metaxas 2006;Grandvalet et al 2008). Although related in some sense, this task should not be mistaken by open set recognition.…”

Section: Introductionmentioning

confidence: 99%

Weightless neural networks for open set recognition

2017

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Open set recognition is a classification-like task. It is accomplished not only by the identification of observations which belong to targeted classes (i.e., the classes among those represented in the training sample which should be later recognized) but also by the rejection of inputs from other classes in the problem domain. The need for proper handling of elements of classes beyond those of interest is frequently ignored, even in works found in the literature. This leads to the improper development of learning systems, which may obtain misleading results when evaluated in their test beds, consequently failing to keep the performance level while facing some real challenge. The adaptation of a classifier for open set recognition is not always possible: the probabilistic premises most of them are built upon are not valid in a openset setting. Still, this paper details how this was realized for WiSARD a weightless artificial neural network model. Such achievement was based on an elaborate distance-like computation this model provides and the definition of rejection thresholds during training. The pro-Editors: Thomas Gärtner, Mirco Nanni, Andrea Passerini, and Celine Robardet. Douglas O. Cardoso thanks CAPES (process 99999.005992/2014-01) and CNPq for financial support. João Gama thanks the support of the European Commission through the project MAESTRA (Grant Number ICT-750 2013-612944). Felipe M. G. França thanks the support of FAPERJ, FINEP and INOVAX.

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Mitigating Concept Drift via Rejection

Göpfert

Hammer

Wersing

2018

Lecture Notes in Computer Science

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Learning in non-stationary environments is challenging, because under such conditions the common assumption of independent and identically distributed data does not hold; when concept drift is present it necessitates continuous system updates. In recent years, several powerful approaches have been proposed. However, these models typically classify any input, regardless of their confidence in the classification -a strategy, which is not optimal, particularly in safety-critical environments where alternatives to a (possibly unclear) decision exist, such as additional tests or a short delay of the decision. Formally speaking, this alternative corresponds to classification with rejection, a strategy which seems particularly promising in the context of concept drift, i.e. the occurrence of situations where the current model is wrong due to a concept change. In this contribution, we propose to extend learning under concept drift with rejection. Specifically, we extend two recent learning architectures for drift, the self-adjusting memory architecture (SAM-kNN) and adaptive random forests (ARF), to incorporate a reject option, resulting in highly competitive state-of-the-art technologies. We evaluate their performance in learning scenarios with different types of drift.

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Optimal local rejection for classifiers

Cited by 33 publications

References 37 publications

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds

Weightless neural networks for open set recognition

Mitigating Concept Drift via Rejection

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