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A Data Centric HitL Framework for Conducting aSsystematic Error Analysis of NLP Datasets using Explainable AI. [PDF]

Sci Rep
El-Sayed A, Nasr A, Mohamed Y, Alaaeldin A, Ali M, Salah O, Khalid A, Lazem S.   +7 more
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Binary naive possibilistic classifiers: Handling uncertain inputs

International Journal of Intelligent Systems, 2009
Summary: Possibilistic networks are graphical models particularly suitable for representing and reasoning with uncertain and incomplete information. According to the underlying interpretation of possibilistic scales, possibilistic networks are either quantitative (using product-based conditioning) or qualitative (using min-based conditioning).
Benferhat, Salem, Tabia, Karim
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Handling missing values in support vector machine classifiers

Neural Networks, 2005
This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved.
Pelckmans, K., De Brabanter, J., Suykens, J. A. K., De Moor, B.   +3 more
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Using Classifier diversity to handle label noise

2015 International Joint Conference on Neural Networks (IJCNN), 2015
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data ...
Michael R. Smith, Tony Martinez
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HANDLING AMBIGUOUS VALUES IN INSTANCE-BASED CLASSIFIERS

International Journal on Artificial Intelligence Tools, 2008
In an attempt to automate evaluation of network intrusion detection systems, we encountered the problem of ambiguously described learning examples. For instance, an attribute's value, or a class label, in a given example was known to be a or b but definitely not c or d.
HANS HOLLAND, MIROSLAV KUBAT, JAN ŽIŽKA   +2 more
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Occlusion Handling via Random Subspace Classifiers for Human Detection

IEEE Transactions on Cybernetics, 2014
This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance.
Javier, Marín, David, Vázquez, Antonio M, López, Jaume, Amores, Ludmila I, Kuncheva   +4 more
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Classifying diverse manual material handling tasks using a single wearable sensor

Applied Ergonomics, 2021
The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a "user-friendly" approach is needed to promote this approach to quantify physical demands in actual workplaces.
Porta, Micaela, Kim, Sunwook, Pau, Massimiliano, Nussbaum, Maury A.   +3 more
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Handling Concept Drifts Using Dynamic Selection of Classifiers

2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016
This work describes the Dynse framework, which uses dynamic selection of classifiers to deal with concept drift. Basically, classifiers trained on new supervised batches available over time are add to a pool, from which is elected a custom ensemble for each test instance during the classification time.
Paulo R. Lisboa De Almeida, Luiz S. Oliveira, Alceu De Souza Britto, Robert Sabourin   +3 more
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