Fast SVM classifier for large-scale classification problems

Wang, Huajun; Li, Genghui; Wang, Zhenkun

doi:10.1016/j.ins.2023.119136

Cited by 37 publications

(10 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Background Methods: A supervised learning model called the Support Vector Machine (SVM) examines data for regression and classification (H. Wang, [35]). An SVM algorithm creates a model that places new examples into one of two categories based on a set of training examples that have been labeled as belonging to one of the two categories.…”

Section: Methodsmentioning

confidence: 99%

"A Novel Sample-Based Classification Strategy to Decipher the Genetic Similarity of Migraine and Fibromyalgia"

Karadayı Ataş

2024

BJSTR

View full text Add to dashboard Cite

Fibromyalgia and migraine are two complex disorders that significantly impact human health, often leading to chronic pain and a reduced quality of life. The underlying genetic links between these disorders are still not well understood, despite their widespread occurrence. This research is a ground-breaking attempt to clarify the genetic interactions between migraine and fibromyalgia. Our work presents a new methodological strategy by applying a sample-based classification technique that hasn't been used in this situation before. Through a more nuanced analysis of genetic data, this novel approach provides new insights into the shared genetic landscape of these conditions. Furthermore, we have created the first-ever comprehensive gene dataset in the field, covering both migraine and fibromyalgia. Our results point to a possible shared etiological pathway between migraine and fibromyalgia by identifying genetic factors that they have in common. There are numerous implications for this study. Our research opens the door to more focused and efficient treatment approaches by improving our understanding of the genetic foundations of these disorders. Moreover, the innovative methodological contributions of this work offer a framework for further genetic studies in other complex diseases. In summary, this work presents novel tools and approaches that have broad applicability in genetic research, in addition to shedding light on the genetic relationship between fibromyalgia and migraine.

show abstract

Section: Methodsmentioning

confidence: 99%

"A Novel Sample-Based Classification Strategy to Decipher the Genetic Similarity of Migraine and Fibromyalgia"

Karadayı Ataş

2024

BJSTR

View full text Add to dashboard Cite

show abstract

“…Whereas empirical risk minimization aims to minimize the mean squared error on the given dataset, SVC uses the Structural Risk Minimization (SRM) principle to find a hyperplane that separates the dataset with a greater margin. SVC was originally developed for binary classification; however, it has been developed for multiclass problems [48] using a one-vs-all approach.…”

Section: ) Support Vector Classifiermentioning

confidence: 99%

Analyzing Social Exchange Motives With Theory-Driven Data and Machine Learning

Igwe,

Durrheim

2024

IEEE Access

View full text Add to dashboard Cite

This study investigated the capability of machine learning to analyze and predict individuals' motives within an experimental game context. Although humans predict the motives of others to respond appropriately, these motives often overlap and are difficult to tease apart in social exchange contexts. An act of reciprocated favor, for example, could equally be motivated by parochial altruism as by self-interest, and human attributions of motives are notoriously biased. Can machine learning effectively predict motives and offer insights into how individuals prioritize overlapping motives? We analyzed motives in an experimental social exchange game using a Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), and a novel combination of Clustering and Hidden Markov Model (C-HMM). The accuracy, precision, recall, and f1-score were compared in two phases: Phase 1, where individuals focus on a single motive, and Phase 2, where individuals consider multiple motives when making decisions in social exchange. With accuracies of 86.96%, 67.31%, and 70.74% for each class of motives tested in Phase 1, C-HMM outperformed the other models. LR demonstrated the best performance, with an accuracy of 45.57% in Phase 2. Further analysis shows that the strength of relationships with ingroup members is a reliable predictor of reciprocation motives. Our model can be extended to nudging prosocial behavior in human-agent collaborations.

show abstract

“…An SVM algorithm is applied, which employs both regression and utilization effort. It treats every point as the points present in multi-dimensional space, not entirely determined by the features of the data (Wang et al, 2023). Classification is available by detecting the distinct hyperplanes by separating various groups of scattering data points.…”

Section: Support Vector Machinementioning

confidence: 99%

Enhancing Navigation and Object Recognition for Visually Impaired Individuals: A Gradient Support Vector Boosting-based Crossover Golden Jackal Algorithm Approach

Abidi,

Alkhalefah,

Siddiquee

2024

Journal of Disability Research

View full text Add to dashboard Cite

On a global scale, individuals with vision impairments encounter various limitations when it comes to moving around and finding their way independently. Their daily activities are impeded by their limited understanding of their environment while moving about both indoors and outside, where situations are constantly changing. Recent technological breakthroughs have made it possible to create several electronic devices that help visually impaired and disabled people with navigation. These devices encompass navigation systems, obstacle avoidance systems, object localization devices, and orientation assistance systems. They are designed to enhance or substitute conventional aids like guide dogs and white canes. This research work proposes a solution based on the gradient support vector boosting-based crossover golden jackal (GSB-CGJ) algorithm, which integrates various assistive technologies focused on navigation and object recognition, providing intelligent feedback to the user. The developed model focuses on guiding visually impaired individuals, preventing unwanted collisions with obstacles, and generating active feedback. The proposed method consists of three distinct phases. In the input phase, images are acquired from the Image and Video Dataset for Visually Impaired using Intel RealSense Camera. The next stage entails object recognition, which is efficiently carried out using the GSB-CGJ algorithm. The hyperparameters of the support vector machine and adaptive boosting methods are optimized using the golden jackal optimization method, enhancing object recognition ability. At the end, the output phase delivers feedback to the user. The experimental and assessment results validate that the model demonstrates high accuracy in recognizing objects and precision in localizing them. This approach effectively delivers remarkable real-time implementation capability, showcasing better adaptability and reliability while reducing execution time.

show abstract

Fast SVM classifier for large-scale classification problems

Cited by 37 publications

References 38 publications

"A Novel Sample-Based Classification Strategy to Decipher the Genetic Similarity of Migraine and Fibromyalgia"

"A Novel Sample-Based Classification Strategy to Decipher the Genetic Similarity of Migraine and Fibromyalgia"

Analyzing Social Exchange Motives With Theory-Driven Data and Machine Learning

Enhancing Navigation and Object Recognition for Visually Impaired Individuals: A Gradient Support Vector Boosting-based Crossover Golden Jackal Algorithm Approach

Contact Info

Product

Resources

About