Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Aggregates Classification01:29

Aggregates Classification

387
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
387
Force Classification01:22

Force Classification

1.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.6K
Methods of Classification and Identification01:28

Methods of Classification and Identification

206
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
206
Classification of Systems-I01:26

Classification of Systems-I

314
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
314
Classification of Signals01:30

Classification of Signals

903
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
903

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.

Scientific reports·2026
Same author

Variations in the Proteome along the Yellow Pea Processing Chain.

Journal of agricultural and food chemistry·2026
Same author

Adsorbent resin technology for flavour removal in canola protein isolate processing.

Food chemistry·2026
Same author

Application of absorbent resins in protein extraction to reduce off-Flavours in flaxseed proteins.

Food research international (Ottawa, Ont.)·2025
Same author

Investigation of Flavor and Functional Properties of Diverse Yellow Pea Ingredients for Pan Bread Applications.

Journal of food science·2025
Same author

Effects of natural deep eutectic solvents' hydration level, choice of hydrogen bond donor and application of ultrasound on the extraction, anti-nutritional components, structural properties and functionality of canola protein isolates.

Ultrasonics sonochemistry·2025

相关实验视频

Updated: Sep 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

混合功能选择框架用于使用机器学习模型增强信用卡欺诈检测.

Al Mahmud Siam1, Pankaj Bhowmik1, Md Palash Uddin1

  • 1Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.

PloS one
|July 16, 2025
PubMed
概括

本研究引入了一种混合功能选择框架,以改善信用卡欺诈检测. 这种新的方法在不平衡的数据集上提高了机器学习模型的性能,为现实世界的应用提供了实用解决方案.

科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 电子支付已经广泛普及,但越来越多的信用卡欺诈导致了巨大的财务损失.
  • 由于高度不平衡的数据集,检测信用卡欺诈是很困难的,欺诈性交易很少发生.
  • 现有的方法与固有的数据不平衡作斗争,需要改进的特征选择技术.

研究的目的:

  • 提出一种新的混合功能选择框架,以加强基于机器学习的信用卡欺诈检测.
  • 整合皮尔森相关性,信息获取 (IG) 和随机森林重要性 (RFI) 以优化特征选择.
  • 在不同的数据集和各种机器学习算法上验证框架的有效性.

主要方法:

  • 一个混合特征选择框架,结合了皮尔森相关性,信息获取 (IG) 和随机森林重要性 (RFI).
  • 皮尔森相关性消除了冗余的特征,而IG和RFI评估了特征的相关性.
  • 工会运作将选定的特征合并为全面和高效的选择,在PCA转换和现实世界数据集上进行测试.

主要成果:

  • 拟议的混合特征选择框架在五个不同的数据集中显著优于基线方法.
  • 使用机器学习算法实现了卓越的欺诈检测性能,例如随机森林,XGBoost和CatBoost.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

913
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

相关实验视频

Last Updated: Sep 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

913
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
  • 该方法在增强欺诈检测能力方面表现出强度和适应性.
  • 结论:

    • 新的混合功能选择框架为检测信用卡欺诈提供了实用和有效的解决方案.
    • 该方法解决了不平衡数据集的挑战,提高了机器学习模型的准确性.
    • 该框架有可能作为实时决策支持系统,有利于金融行业.