Results 31 to 40 of about 39,918 (213)

Effect of De-noising by Wavelet Filtering and Data Augmentation by Borderline SMOTE on the Classification of Imbalanced Datasets of Pig Behavior

Frontiers in Animal Science, 2021
Classification of imbalanced datasets of animal behavior has been one of the top challenges in the field of animal science. An imbalanced dataset will lead many classification algorithms to being less effective and result in a higher misclassification ...
Min Jin, Chunguang Wang, Dan Børge Jensen   +2 more
doaj   +1 more source

Service-Aware Two-Level Partitioning for Machine Learning-Based Network Intrusion Detection With High Performance and High Scalability

IEEE Access, 2021
A network intrusion detection system (NIDS) is an important technology for cyber security. Recently, machine learning based NIDSs are being actively researched as various machine learning techniques are proposed.
Yeongje Uhm, Wooguil Pak
doaj   +1 more source

Lightweight Micro-Expression Recognition on Composite Database

Applied Sciences, 2023
The potential of leveraging micro-expression in various areas such as security, health care and education has intensified interests in this area. Unlike facial expression, micro-expression is subtle and occurs rapidly, making it imperceptible.
Nur Aishah Ab Razak, Shahnorbanun Sahran
doaj   +1 more source

A Hybrid Sampling SVM Approach to Imbalanced Data Classification

Abstract and Applied Analysis, 2014
Imbalanced datasets are frequently found in many real applications. Resampling is one of the effective solutions due to generating a relatively balanced class distribution.
Qiang Wang
doaj   +1 more source

Research on intrusion detection method of marine meteorological sensor network based on anomalous behaviors

Tongxin xuebao, 2023
To deal with the abnormal data flow attacks faced by the marine meteorological sensor network (MMSN), analyze the security mechanism, and aim at the complex and huge network structure and the extremely imbalanced data flow in the nodes, the intrusion ...
Xin SU, Tian TIAN, Gong Ziyang, Yiqing ZHOU   +3 more
doaj   +2 more sources

A weighted pattern matching approach for classification of imbalanced data with a fireworks-based algorithm for feature selection

Connection Science, 2019
Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class ...
N. K. Sreeja
doaj   +1 more source

RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification

Journal of King Saud University: Computer and Information Sciences, 2022
Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning.
Ahmed Arafa, Nawal El-Fishawy, Mohammed Badawy, Marwa Radad   +3 more
doaj   +1 more source

Performance Improvement of Deep Learning Based Multi-Class ECG Classification Model Using Limited Medical Dataset

IEEE Access, 2023
Medical data often exhibit class imbalance, which poses a challenge in classification tasks. To solve this problem, data augmentation techniques are used to balance the data.
Sanghoon Choi, Hyo-Chang Seo, Min Soo Cho, Segyeong Joo, Gi-Byoung Nam   +4 more
doaj   +1 more source

Assessing model performance in Alzheimer's disease classification: The impact of data imbalance on fine-tuned vision transformers and CNN architectures

Journal of Intelligent Systems
Problem: Data imbalance in medical datasets poses significant challenges for the performance of machine learning models, particularly in classifying Alzheimer’s disease (AD).
Almalki Hassan, Khadidos Alaa O., Alhebaishi Nawaf   +2 more
doaj   +1 more source

A Class Imbalance Loss for Imbalanced Object Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
The class imbalance problem exists widely in vision data. In these imbalanced datasets, the majority classes dominate the loss and influence the gradient.
Linbin Zhang, Caiguang Zhang, Sinong Quan, Huaxin Xiao, Gangyao Kuang, Li Liu   +5 more
doaj   +1 more source