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Extreme Learning Machine Combining Hidden-Layer Feature Weighting and Batch Training for Classification
Based on the network structure and training methods of extreme learning machines, extreme learning machine combining hidden-layer feature weighting...
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Ensemble clustering and feature weighting in time series data
Ensemble clustering is an important approach in machine learning, which combines multiple hypotheses to minimize the risk of selecting a wrong...
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Sensitivity analysis of feature weighting for classification
Feature weighting is a well-known approach for improving the performance of machine learning algorithms that has been gaining a lot of traction...
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A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data
Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality...
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A correlation-based feature weighting filter for multi-label Naive Bayes
Multi-label classification is used to solve the problem where multiple labels are associated with single sample. Naive Bayes (NB) classifier is...
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Intelligent Integration of Diversified Retirement Information Based on Feature Weighting
When carrying out intelligent integration of retirement information, the existing data processing and analysis architecture can no longer meet the...
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DFW-PP: dynamic feature weighting-based popularity prediction for social media content
The increasing popularity of social media platforms makes it important to study user engagement, which is a crucial aspect of any marketing strategy...
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Breast Cancer Detection Based on Modified Harris Hawks Optimization and Extreme Learning Machine Embedded with Feature Weighting
Computer-aided diagnosis (CAD) can assist doctors with clinical diagnosis and improve diagnosis accuracy and efficiency further. It is significative...
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Weighting Approaches in Data Mining and Knowledge Discovery: A Review
Modeling and forecasting are impressive and active research areas, which have been widely used in diverse theoretical and practical applications,...
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An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets
Breast cancer is a major threat, predominantly affecting the female population. Staging of cancer enables early detection and prognosis of patients,...
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Instance Weighting Methods
Instance weighting methods are one of the most effective methods for transfer learning. Technically speaking, any weighting methods can be used for...
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An efficient malware detection approach with feature weighting based on Harris Hawks optimization
This paper introduces and tests a novel machine learning approach to detect Android malware. The proposed approach is composed of Support Vector...
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Dynamic feature weighting for multi-label classification problems
This paper proposes a dynamic feature weighting approach for multi-label classification problems. The choice of dynamic weights plays a vital role in...
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Assessing action quality with semantic-sequence performance regression and densely distributed sample weighting
Action Quality Assessment (AQA) is a critical branch of video understanding, offering impartial evaluations for competitive sports. Existing...
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Sample-level weighting for multi-task learning with auxiliary tasks
Multi-task learning (MTL) can improve the generalization performance of neural networks by sharing representations with related tasks. Nonetheless,...
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Probabilistic Term Weighting Based on Three-Way Decisions for Class Based Feature Selection
Selecting features that represent a specific class is important to achieve a high text classification performance. The core and critical part of any...
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Attentional weighting strategy-based dynamic GCN for skeleton-based action recognition
Graph Convolutional Networks (GCNs) have become the standard skeleton-based human action recognition research paradigm. As a core component in graph...
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An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection
Computer vision-based detection approaches have been widely used in defect inspection tasks. However, identifying small-sized defects is still a...
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Automatic label assignment object detection mehtod on only one feature map
Most deep learning-based object detection methods are proposed based on multi-level feature environments. Although some researchers have tried to...
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Boosting Out-of-Distribution Detection with Sample Weighting
To enhance the reliability of machine learning models in the open world, there is considerable interest in detecting out-of-distribution (OOD) inputs...