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On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey
Abstract Introduction The number of Alzheimer's Disease (AD) patients is increasing with
increased life expectancy and 115.4 million people are expected to be affected in 2050 …
increased life expectancy and 115.4 million people are expected to be affected in 2050 …
Confident learning: Estimating uncertainty in dataset labels
Learning exists in the context of data, yet notions of confidence typically focus on model
predictions, not label quality. Confident learning (CL) is an alternative approach which …
predictions, not label quality. Confident learning (CL) is an alternative approach which …
Trusting my predictions: on the value of instance-level analysis
Machine Learning solutions have spread along many domains, including critical
applications. The development of such models usually relies on a dataset containing …
applications. The development of such models usually relies on a dataset containing …
Classification in the presence of label noise: a survey
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 …
SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets
Canonical machine learning algorithms assume that the number of objects in the considered
classes are roughly similar. However, in many real-life situations the distribution of examples …
classes are roughly similar. However, in many real-life situations the distribution of examples …
Learning deep networks from noisy labels with dropout regularization
Large datasets often have unreliable labels-such as those obtained from Amazon's
Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets …
Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets …
On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems
Security policies of information systems and networks are designed for maintaining the
integrity of both the confidentiality and availability of the data for their trusted users …
integrity of both the confidentiality and availability of the data for their trusted users …
Enabling smart data: noise filtering in big data classification
In any knowledge discovery process the value of extracted knowledge is directly related to
the quality of the data used. Big Data problems, generated by massive growth in the scale of …
the quality of the data used. Big Data problems, generated by massive growth in the scale of …
Radial-based oversampling for noisy imbalanced data classification
Imbalanced data classification remains a focus of intense research, mostly due to the
prevalence of data imbalance in various real-life application domains. A disproportion …
prevalence of data imbalance in various real-life application domains. A disproportion …