Robust supervised classification with mixture models: Learning from data with uncertain labels

Bouveyron, Charles; Girard, Sabine

doi:10.1016/j.patcog.2009.03.027

Cited by 119 publications

(72 citation statements)

References 23 publications

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“…One is to filter the noisy label examples to either remove or correct them [109]. Multiple techniques have been employed to detect noisy labels, such as large margin classifiers [110], nearest neighborhood verification [111], committee voting [112], cross validation [113], and clustering algorithms [114,115]. Another type learns directly from the weakly labeled dataset by considering the mechanism of label noise.…”

Section: Noisy Label Learningmentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Wang¹,

Zhao²,

Huang³

et al. 2017

Preprint

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The concept of 'big data' has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.

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Section: Noisy Label Learningmentioning

confidence: 99%

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Wang¹,

Zhao²,

Huang³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…However, it is difficult to judge labeling errors in the subjective domains, because the absolute ground truth is unknown. In [9], authors propose to use a unsupervised mixture model in which the supervised information is introduced, so as to compare the supervised information given by the learning data with an unsupervised modelling. For this model, the probability that the jth cluster belongs to the ith class is introduced to measure the consistency between classes and clusters.…”

Section: Related Workmentioning

confidence: 99%

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

Dai¹

2017

JCC

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In order to improve the performance of classifiers in subjective domains, this paper defines a metric to measure the quality of the subjectively labelled training data (QoSTD) by means of K-means clustering. Then, the QoSTD is used as a weight of the predicted class scores to adjust the likelihoods of instances. Moreover, two measurements are defined to assess the performance of the classifiers trained by the subjective labelled data. The binary classifiers of Traditional Chinese Medicine (TCM) Zhengs are trained and retrained by the real-world data set, utilizing the support vector machine (SVM) and the discrimination analysis (DA) models, so as to verify the effectiveness of the proposed method. The experimental results show that the consistency of likelihoods of instances with the corresponding observations is increased notable for the classes, especially in the cases with the relatively low QoSTD training data set. The experimental results also indicate the solution how to eliminate the miss-labelled instances from the training data set to re-train the classifiers in the subjective domains.

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“…However, when class label noise is present, it becomes unclear why would CV be a good approach since then all candidate models will be validated against noisy class labels. The issue has also been briefly discussed in [24,6]. In [24], the authors resort to using a 'trusted validation set' to select optimal kernel parameters.…”

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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Robust supervised classification with mixture models: Learning from data with uncertain labels

Cited by 119 publications

References 23 publications

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains

Learning kernel logistic regression in the presence of class label noise

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