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Automated multi-class skin cancer classification using white shark optimizer with ensemble learning classifier on dermoscopy images
The most prevalent cancer around the world is Skin cancer (SC). Clinical assessment of skin lesions is essential to evaluate the features of the...
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Explaining deep multi-class time series classifiers
Explainability helps users trust deep learning solutions for time series classification. However, existing explainability methods for multi-class...
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Multi-label learning for identifying co-occurring class code smells
Code smell identification is crucial in software maintenance. The existing literature mostly focuses on single code smell identification. However, in...
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Online active learning method for multi-class imbalanced data stream
In the field of data mining, data stream classification is an important research direction. However, the presence of issues such as multi-class...
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Multi-Class, Multi-Label and Multi-Target in Wineinformatics
“Can wine grade, price and region being predicted altogether with higher accuracy?” In previous chapters, bi-class classification and regression were...
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MVDet: multi-view multi-class object detection without ground plane assumption
Although many state-of-the-art methods of object detection in a single image have achieved great success in the last few years, they still suffer...
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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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Semi supervised K–SVCR for multi-class classification
In recent developments, the traditional binary class SVM has evolved into a multi-class classifier utilizing a ‘1-versus-1-versus-rest’ approach...
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One-class classifier based on principal curves
One-class classification is a special multi-class approach where data from only a single class are available for classifier training. It is an...
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Learning sample representativeness for class-imbalanced multi-label classification
Class imbalance is a common problem that often occurs in multi-label image classification. In multi-label datasets, the co-occurrence of labels...
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Learning label-specific features for decomposition-based multi-class classification
Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules, e.g.,...
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Multi-class nonparallel support vector machine
In this paper, we propose an extended version of Nonparallel Support Vector Machine (NPSVM) for multi-classification using one-versus-one-versus-rest...
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Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification
Real-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major...
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Multi-class intrusion detection system in SDN based on hybrid BiLSTM model
Software-defined networking (SDN) is a new network paradigm, which is highly decoupled compared to traditional networks, and makes it easier to...
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DynaQ: online learning from imbalanced multi-class streams through dynamic sampling
Online supervised learning from fast-evolving data streams, particularly in domains such as health, the environment, and manufacturing, is a crucial...
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Classifier subset selection based on classifier representation and clustering ensemble
Ensemble pruning can improve the performance and reduce the storage requirements of an integration system. Most ensemble pruning approaches remove...
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Quantum Variational Multi-class Classifier for the Iris Data Set
Recent advances in machine learning on quantum computers have been made possible mainly by two discoveries. Map** the features into exponentially...
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Multi-class classification of COVID-19 documents using machine learning algorithms
In most biomedical research paper corpus, document classification is a crucial task. Even due to the global epidemic, it is a crucial task for...
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Deep reinforcement learning for multi-class imbalanced training: applications in healthcare
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the...
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Ghostbusters: How the Absence of Class Pairs in Multi-Class Multi-Label Datasets Impacts Classifier Accuracy
Compositional bias is common in Multi-Class Multi-Label datasets where certain classes frequently co-occur together. Classification performance due...