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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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Amelioration of linguistic semantic classifier with sentiment classifier manacle for the focused web crawler
Sentiment relevant information in the web pages concerning products, establishment, and commodities concentrates principally on the available textual...
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Image Neural Network Classifier by Detail Attributes
AbstractThe paper presents the features of the development of a neural network analyzer of image detail. The neural network analyzer is a binary...
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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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Using the Grasshopper Optimization Algorithm for Fuzzy Classifier Design
AbstractThe paper describes three stages in the construction of a fuzzy classifier. The first refers to the formation of fuzzy rules, the second...
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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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Distributed adaptive nearest neighbor classifier: algorithm and theory
When data is of an extraordinarily large size or physically stored in different locations, the distributed nearest neighbor (NN) classifier is an...
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Classifier-dependent feature selection via greedy methods
The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature...
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Ensemble learning with weighted voting classifier for melanoma diagnosis
Melanoma, the most lethal type of skin cancer, presents a substantial public health challenge. Detecting melanoma promptly is paramount for enhancing...
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ConvBiGRU deep learning classifier for sentiment analysis with optimization algorithm
Nowadays sentiment analysis is more familiar in the research field. It includes two methods, which are lexicon-based method and machine...
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Reinforcement learning-based cost-sensitive classifier for imbalanced fault classification
Fault classification plays a crucial role in the industrial process monitoring domain. In the datasets collected from real-life industrial processes,...
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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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Contrast with major classifier vectors for federated medical relation extraction with heterogeneous label distribution
Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data. To...
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Effective Elytron Vespid-B rank BiLSTM classifier for Multi-Document Summarization
Multi-Document Summarization is the progression of extracting the pertinent information from a group of documents and weeding out the irrelevant...
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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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Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning
Different from Unsupervised Domain Adaptation (UDA), Source-Free Unsupervised Domain Adaptation (SFUDA) transfers source knowledge to target domain...
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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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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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Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images
Designing a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet...
