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A multiple classifiers system with roulette-based feature subspace selection for one-vs-one scheme
Classification is one of the most important topics in machine learning. However, most of these works focus on the two-class classification (i.e.,...
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Unsupervised few-shot image classification via one-vs-all contrastive learning
Human beings innately possess the ability to perceive novel concepts from only a few samples. As a setting to imitate the learned ability of human...
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Subconcept perturbation-based classifier for within-class multimodal data
In classification, it is generally assumed that data from one class consist of one pure compact data cluster. However, in many cases, this cluster...
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Meta-classifier free negative sampling for extreme multilabel classification
Negative sampling is a common approach for making the training of deep models in classification problems with very large output spaces, known as...
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Classifier selection using geometry preserving feature
The selection of proper classifiers for a given data set is full of challenges. The critical problem of classifier selection is how to extract...
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Towards kernelizing the classifier for hyperbolic data
Data hierarchy, as a hidden property of data structure, exists in a wide range of machine learning applications. A common practice to classify such...
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Transferring Vision-Language Models for Visual Recognition: A Classifier Perspective
Transferring knowledge from pre-trained deep models for downstream tasks, particularly with limited labeled samples, is a fundamental problem in...
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Softmin discrete minimax classifier for imbalanced classes and prior probability shifts
This paper proposes a new approach for dealing with imbalanced classes and prior probability shifts in supervised classification tasks. Coupled with...
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Cross-domain Fisher Discrimination Criterion: A Domain Adaptive Method Based on the Nature of Classifier
Most domain adaptive methods enhance the classification performance of target domain via overcoming the distribution difference between source and...
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Towards adaptive unknown authentication for universal domain adaptation by classifier paradox
Universal domain adaptation (UniDA) is a general unsupervised domain adaptation setting, which addresses both domain and label shifts in adaptation....
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Automatic diagnosis of CoV-19 in CXR images using haar-like feature and XgBoost classifier
Many researchers and medical practitioners have recently focused on the automatic detection of COVID-19 using chest X-Ray (CXR) images. In the past,...
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Pneumonia detection in chest x-ray images using an optimized ensemble with XGBoost classifier
Pneumonia is regarded as the top killer of children amongst all other infectious diseases by causing nearly 700,000 deaths to children aged under...
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A deep learning-based histopathology classifier for Focal Cortical Dysplasia
A light microscopy-based histopathology diagnosis of human brain specimens obtained from epilepsy surgery remains the gold standard to confirm the...
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Enhanced cloud security: a novel intrusion detection system using ARSO algorithm and Bi-LSTM classifier
The cloud computing environment faces significant security challenges, impacting its long-term growth. Intrusion detection is crucial for mitigating...
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Real-time invasive sea lamprey detection using machine learning classifier models on embedded systems
Invasive sea lamprey ( Petromyzon marinus ) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to...
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Design of accent classifier based on speech rhythm features
Recognition systems suffer from significant performance degradation when operating in foreign accent conditions. Speech rhythm, which is considered...
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Classifier calibration: a survey on how to assess and improve predicted class probabilities
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated...
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Feature mining and classifier selection for API calls-based malware detection
This paper deals with a major challenge in cyber-security: the need to respond to ever renewed techniques used by attackers in order to avoid...
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Uncertainty-aware non-invasive patient–ventilator asynchrony detection using latent Gaussian mixture generative classifier with noisy label correction
Patient–ventilator asynchrony (PVA) refers to instances where a mechanical ventilator’s cycles are desynchronised from the patient’s breathing...
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An intelligence method for heart disease prediction using integrated filter-evolutionary search based feature selection and optimized ensemble classifier
Heart disease is more difficult to detect due to certain risk factors such as diabetes, high blood pressure, abnormal heart rate, high cholesterol,...
