Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

60
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
60
Randomized Experiments01:13

Randomized Experiments

6.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.7K
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.0K
Random and Systematic Errors01:20

Random and Systematic Errors

10.8K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Random Variables01:09

Random Variables

11.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

TSM-NIDS: A time-series mixer-based intrusion detection system for IoT networks.

MethodsX·2026
Same author

A hybrid TCN-GRU model for classifying human activities using smartphone inertial signals.

PloS one·2024
Same author

In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol.

F1000Research·2023
Same author

The Development of a Data Collection and Browser Fingerprinting System.

Sensors (Basel, Switzerland)·2023
Same author

MSTCN: A multiscale temporal convolutional network for user independent human activity recognition.

F1000Research·2023
Same author

Stacked deep analytic model for human activity recognition on a UCI HAR database.

F1000Research·2022
Same journal

From pixels to length: Body length estimation of aquatic macroinvertebrates from digital images for ecological applications.

MethodsX·2026
Same journal

Sorbent-coated metal discs for time-integrated VOC sampling: A reproducible workflow coupled to SPME-GC/MS.

MethodsX·2026
Same journal

Step-by-step <i>En face</i> O red oil method for aortic plaque staining and quantification in ApoE knockout mouse.

MethodsX·2026
Same journal

Optimized protocols for culturing and sectioning mouse intestinal organoids: enhancing efficiency and structural integrity.

MethodsX·2026
Same journal

MCLF: Montage consistent CNN-Liquid fusion for long-term scalp EEG seizure detection.

MethodsX·2026
Same journal

Facile synthesis of model polystyrene nanoparticles for nanoplastics research.

MethodsX·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 5, 2025

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

975

Bayesian optimization driven strategy for detecting credit card fraud with Extremely Randomized Trees.

Zheng You Lim1, Ying Han Pang1, Khairul Zaqwan Bin Kamarudin1

  • 1Faculty of Information Science and Technology, Multimedia University, Ayer Keroh, Melaka 75450, Malaysia.

Methodsx
|December 6, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI tool, Bayesian-optimized Extremely Randomized Trees (TP-ERT), for advanced credit card fraud detection. TP-ERT significantly improves accuracy in identifying fraudulent transactions compared to existing systems.

Keywords:
Credit card fraud detectionExtremely Randomized TreesMachine learningOptimizationTP-ERT: TPE-optimized Extremely Randomized TreesTree-structured Parzen Estimator

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.0K

Related Experiment Videos

Last Updated: Jun 5, 2025

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

975
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.0K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Rising credit card usage increases fraud risks.
  • Conventional fraud detection models struggle with complex, imbalanced data, leading to overfitting.
  • Advanced machine learning is crucial for effective credit card fraud detection (CCFD).

Purpose of the Study:

  • To propose a novel Bayesian-optimized Extremely Randomized Trees (TP-ERT) model for enhanced credit card fraud detection.
  • To improve model generalization and capture diverse transaction patterns.
  • To evaluate TP-ERT's performance against existing CCFD systems.

Main Methods:

  • Utilized Extremely Randomized Trees with enhanced randomness in split points and feature selection.
  • Employed Tree-structured Parzen Estimator (TPE), a Bayesian optimization strategy, for hyperparameter tuning.
  • Assessed model performance on a real-world credit card transaction dataset.

Main Results:

  • The proposed TP-ERT model demonstrated superior performance over other CCFD systems.
  • TP-ERT achieved a higher F1 score, validating its effectiveness.
  • Bayesian optimization using TPE proved more efficient than other techniques for hyperparameter tuning.

Conclusions:

  • The optimized Extremely Randomized Trees model is a viable artificial intelligence tool for detecting credit card fraud.
  • Hyperparameter tuning via Tree-structured Parzen Estimator effectively enhances model performance by capturing intricate transaction patterns.
  • Empirical findings confirm the proposed approach's superiority on real-world data.