Results 31 to 40 of about 13,178 (146)

Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data

Diagnostics
Background: Surgical site infections (SSIs) lead to higher hospital readmission rates and healthcare costs, representing a significant global healthcare burden.
Salha Al-Ahmari, Farrukh Nadeem
doaj   +1 more source

Enhancing Phishing Email Detection through Ensemble Learning and Undersampling

Applied Sciences, 2023
In real-world scenarios, the number of phishing and benign emails is usually imbalanced, leading to traditional machine learning or deep learning algorithms being biased towards benign emails and misclassifying phishing emails.
Qinglin Qi, Zhan Wang, Yijia Xu, Yong Fang, Changhui Wang   +4 more
doaj   +1 more source

Resampling imbalanced data for network intrusion detection datasets

Journal of Big Data, 2021
Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
doaj   +1 more source

Random Walk-steered Majority Undersampling

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes. Rather than marking the majority points which belong to the neighborhood of a few minority points, we are interested to perceive the closeness of the majority points to the ...
Sadhukhan, Payel, Pakrashi, Arjun, Mac Namee, Brian   +2 more
openaire   +2 more sources

Orchid Species Classification Using the DenseNet121 Deep Learning Model with a Data Imbalance Handling Approach

Journal of Applied Informatics and Computing
For conservation, commercial cultivation, and scientific research, accurate identification of orchid species often requires specialized expertise.
Fadhilah Aditya Akbar, Christy Atika Sari   +1 more
doaj   +1 more source

A Novel 3D Infrared Tomographic Technology Based on Undersampling and Line-Scanned Structured Heating

Proceedings
Traditional infrared thermography (IRT) techniques can only provide two-dimensional (2D) projections of surface temperatures, and it is difficult to intuitively present the surface profile of the three-dimensional (3D) structure and the spatial ...
Rongbang Wang, Hai Zhang, Xavier Maldague   +2 more
doaj   +1 more source

On Generalized Schürmann Entropy Estimators

Entropy, 2022
We present a new class of estimators of Shannon entropy for severely undersampled discrete distributions. It is based on a generalization of an estimator proposed by T.
Peter Grassberger
doaj   +1 more source

Zero-Inflated Text Data Analysis Using Imbalanced Data Sampling and Statistical Models

Computers
Text data often exhibits high sparsity and zero inflation, where a substantial proportion of entries in the document–keyword matrix are zeros. This characteristic presents challenges to traditional count-based models, which may suffer from reduced ...
Sunghae Jun
doaj   +1 more source

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Engineering Reports, 2021
This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real‐world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to ...
Dr. Seba Susan, Amitesh Kumar
doaj   +1 more source

Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems

Applied Sciences, 2014
Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem.
Nikos Petrellis
doaj   +1 more source