Contextualizing Naive Bayes Predictions

Loor, Marcelo; Tré, Guy De

doi:10.1007/978-3-030-50153-2_60

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Naı ¨ve Bayes classifiers are built upon conditional probabilities. The term 'naı ¨ve' refers to the assumption that features are mutually exclusive, allowing the estimation of their individual contribution to the output [35]. These models can also be used as standalone, interpretable classifiers, as has been demonstrated in DR classification through decision trees by Mane and Jadhav, and Naı ¨ve Bayes by Harangi et al [36,37].…”

Section: Model-centric: Surrogatesmentioning

confidence: 99%

Applications of interpretability in deep learning models for ophthalmology

Hanif

Beqiri

Keane

et al. 2021

Current Opinion in Ophthalmology

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Purpose of review:In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. Recent findings:The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users' ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models' "interpretability" and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models.Summary: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.

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Section: Model-centric: Surrogatesmentioning

confidence: 99%

Applications of interpretability in deep learning models for ophthalmology

Hanif

Beqiri

Keane

et al. 2021

Current Opinion in Ophthalmology

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“…Since such an AIFS is used inside Algorithm 1, a learning process and an evaluation process are needed to obtain K A@E j andÂ @E j respectively. For the sake of illustration, the learning process and the augmented evaluation process applied in explainable support vector machine classification (XSVMC) [36] have been used for this simulation-other techniques like those proposed in [37] can also be applied.…”

Section: Simulationmentioning

confidence: 99%

Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

2021

Self Cite

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A flexible attribute-set group decision-making (FAST-GDM) problem consists in finding the most suitable option(s) out of the options under consideration, with a general agreement among a heterogeneous group of experts who can focus on different attributes to evaluate those options. An open challenge in FAST-GDM problems is to design consensus reaching processes (CRPs) by which the participants can perform evaluations with a high level of consensus. To address this challenge, a novel algorithm for reaching consensus is proposed in this paper. By means of the algorithm, called FAST-CR-XMIS, a participant can reconsider his/her evaluations after studying the most influential samples that have been shared by others through contextualized evaluations. Since exchanging those samples may make participants’ understandings more like each other, an increase of the level of consensus is expected. A simulation of a CRP where contextualized evaluations of newswire stories are characterized as augmented intuitionistic fuzzy sets (AIFS) shows how FAST-CR-XMIS can increase the level of consensus among the participants during the CRP.

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“…In this regard, XSVMC can be considered to be part of the "transparent algorithms" identified by the above-mentioned survey. The method proposed by Loor and De Tré to contextualize naive Bayes predictions [28] is another example of such transparent algorithms.…”

Section: Related Workmentioning

confidence: 99%

Contextualizing Support Vector Machine Predictions

Loor¹,

Tré²

2020

IJCIS

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Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context. A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization.

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Contextualizing Naive Bayes Predictions

Cited by 6 publications

References 20 publications

Applications of interpretability in deep learning models for ophthalmology

Applications of interpretability in deep learning models for ophthalmology

Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

Contextualizing Support Vector Machine Predictions

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