Decontamination of Training Samples for Supervised Pattern Recognition Methods

Barandela, Ricardo; Gasca, Eduardo

doi:10.1007/3-540-44522-6_64

Cited by 69 publications

(40 citation statements)

References 19 publications

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“…Signals with samples that have been clipped are removed using a clipping test [6], [18]. A signal having few samples with large deviation from their mean value is said to have type-A artifacts where as a signal having large number of samples with very little If a channel has stuck at fault, the EPs of that channel are discarded from further analysis.…”

Section: Artifact Detection Strategymentioning

confidence: 99%

Detection of Type-B Artifacts in VEPs using Median Deviation Algorithm

Reddy¹,

Reddy²

2017

IOSR JECE

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Section: Artifact Detection Strategymentioning

confidence: 99%

Detection of Type-B Artifacts in VEPs using Median Deviation Algorithm

Reddy¹,

Reddy²

2017

IOSR JECE

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“…The seemingly straightforward approach is by means of data preprocessing where any suspect samples are removed or relabelled [7,1,29,37,31,18]. However, these approaches hold the risk of removing useful data too, which is detrimental to the classification performance, especially when the number of training examples is limited (e.g.…”

Section: Introductionmentioning

confidence: 99%

Learning kernel logistic regression in the presence of class label noise

Bootkrajang

Kabán

2014

Pattern Recognition

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The classical machinery of supervised learning machines relies on a correct set of training labels. Unfortunately, there is no guarantee that all of the labels are correct. Labelling errors are increasingly noticeable in today's classification tasks, as the scale and difficulty of these tasks increases so much that perfect label assignment becomes nearly impossible. Several algorithms have been proposed to alleviate the problem, of which a robust Kernel Fisher Discriminant is a successful example. However, for classification, discriminative models are of primary interest, and rather curiously, the very few existing label-robust discriminative classifiers are limited to linear problems.In this paper, we build on the widely used and successful kernelising technique to introduce a label-noise robust Kernel Logistic Regression classifier. The main difficulty that we need to bypass is how to determine the model complexity parameters when no trusted validation set is available. We propose to adapt the Multiple Kernel Learning approach for this new purpose, together with a Bayesian regularisation scheme. Empirical results on 13 benchmark data sets and two real-world applications demonstrate the success of our approach.

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“…Clean classification models are achieved through elimination of outlier samples [16] or through active learning processes where the learner actively chooses the most informative training samples from a pool of previously labeled samples and hence steers the choice of expert labels. Active learning methods have demonstrated value where large training datasets are available, such as in remote sensing [17], text classification [18,19] and object recognition applications.…”

Section: Introductionmentioning

confidence: 99%

Active Relearning for Robust Supervised Training of Emphysema Patterns

et al. 2014

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Radiologists are adept at recognizing the character and extent of lung parenchymal abnormalities in computed tomography (CT) scans. However, the inconsistent differential diagnosis due to subjective aggregation necessitates the exploration of automated classification based on supervised or unsupervised learning. The robustness of supervised learning depends on the training samples. Towards optimizing emphysema classification, we introduce a physician-in-the-loop feedback approach to minimize ambiguity in the selected training samples. An experienced thoracic radiologist selected 412 regions of interest (ROIs) across 15 datasets to represent 124, 129, 139 and 20 training samples of mild, moderate, severe emphysema and normal appearance, respectively. Using multi-view (multiple metrics to capture complementary features) inductive learning, an ensemble of seven unoptimized support vector models (SVM) each based on a specific metric was constructed in less than 6 s. The training samples were classified using seven SVM models and consensus labels were created using majority voting. In the active relearning phase, the ensemble-expert label conflicts were resolved by the expert. The efficacy and generality of active relearning feedback was assessed in the optimized parameter space of six general purpose classifiers across the seven dissimilarity metrics. The proposed just-in-time active relearning feedback with un-optimized SVMs yielded 15 % increase in classification accuracy and 25 % reduction in the number of support vectors. The average improvement in accuracy of six classifiers in their optimized parameter space was 21 %. The proposed cooperative feedback method enhances the quality of training samples used to construct automated classification of emphysematous CT scans. Such an approach could lead to substantial improvement in quantification of emphysema.

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Decontamination of Training Samples for Supervised Pattern Recognition Methods

Cited by 69 publications

References 19 publications

Detection of Type-B Artifacts in VEPs using Median Deviation Algorithm

Detection of Type-B Artifacts in VEPs using Median Deviation Algorithm

Learning kernel logistic regression in the presence of class label noise

Active Relearning for Robust Supervised Training of Emphysema Patterns

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