ArticleFrontiers in artificial intelligence2024
A distance-based kernel for classification via Support Vector Machines.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
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- Can urinary exosomal lncRNAs HOTAIR and MALAT1 predict surgical need and outcomes in unilateral antenatal hydronephrosis?Pediatric surgery international · 2026Article
- Machine learning applications for predicting safety incidents in construction industry.Scientific reports · 2026Article
- Identification of genes associated with smell dysfunction in Parkinson's disease.Clinical parkinsonism & related disorders · 2026Article
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Detection of Aspergilloma Disease Using Feature-Selection-Based Vision Transformers.Diagnostics (Basel, Switzerland) · 2024Article
- Detection of Thymoma Disease Using mRMR Feature Selection and Transformer Models.Diagnostics (Basel, Switzerland) · 2024Article
- Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.
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