[HTML][HTML] SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
H Sanz, C Valim, E Vegas, JM Oller, F Reverter - BMC bioinformatics, 2018 - Springer
Background Support vector machines (SVM) are a powerful tool to analyze data with a
number of predictors approximately equal or larger than the number of observations …
number of predictors approximately equal or larger than the number of observations …
Classification in the presence of label noise: a survey
B Frénay, M Verleysen - IEEE transactions on neural networks …, 2013 - ieeexplore.ieee.org
Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …
consequences. For example, the accuracy of predictions may decrease, whereas the …
Learning uncertainty with artificial neural networks for predictive process monitoring
H Weytjens, J De Weerdt - Applied Soft Computing, 2022 - Elsevier
The inability of artificial neural networks to assess the uncertainty of their predictions is an
impediment to their widespread use. We distinguish two types of learnable uncertainty …
impediment to their widespread use. We distinguish two types of learnable uncertainty …
Active cleaning of label noise
Mislabeled examples in the training data can severely affect the performance of supervised
classifiers. In this paper, we present an approach to remove any mislabeled examples in the …
classifiers. In this paper, we present an approach to remove any mislabeled examples in the …
New label noise injection methods for the evaluation of noise filters
Noise is often present in real datasets used for training Machine Learning classifiers. Their
disruptive effects in the learning process may include: increasing the complexity of the …
disruptive effects in the learning process may include: increasing the complexity of the …
Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment
In the context of manufacturing, support vector machines (SVM) are commonly used to
predict quality, ie, predict the characteristics of a product according to the manufacturing …
predict quality, ie, predict the characteristics of a product according to the manufacturing …
Uncertain data classification with additive kernel support vector machine
Z Xie, Y Xu, Q Hu - Data & Knowledge Engineering, 2018 - Elsevier
In this work, a classification learning algorithm is designed within the framework of support
vector machines through modeling uncertain data with additive kernels, which are …
vector machines through modeling uncertain data with additive kernels, which are …
Kernel-based learning from both qualitative and quantitative labels: application to prostate cancer diagnosis based on multiparametric MR imaging
Building an accurate training database is challenging in supervised classification. For
instance, in medical imaging, radiologists often delineate malignant and benign tissues …
instance, in medical imaging, radiologists often delineate malignant and benign tissues …
Label-noise reduction with support vector machines
The problem of detection of label-noise in large datasets is investigated. We consider
applications where data are susceptible to label error and a human expert is available to …
applications where data are susceptible to label error and a human expert is available to …