Results 51 to 60 of about 39,918 (213)

Adaptive Sampling Framework for Imbalanced DDoS Traffic Classification

Sensors
Imbalanced data is a major challenge in network security applications, particularly in DDoS (Distributed Denial of Service) traffic classification, where detecting minority classes is critical for timely and cost-effective defense.
Hongjoong Kim, Deokhyeon Ham, Kyoung-Sook Moon   +2 more
doaj   +1 more source

Impact of imbalanced features on large datasets

Frontiers in Big Data
The exponential growth of image and video data motivates the need for practical real-time content-based searching algorithms. Features play a vital role in identifying objects within images. However, feature-based classification faces a challenge due to uneven class instance distribution. Ideally, each class should have an equal number of instances and
Waleed Albattah, Rehan Ullah Khan
openaire   +3 more sources

An Improved Method of Detecting Macro Malware on an Imbalanced Dataset

IEEE Access, 2020
In spear-phishing attacks, macro malware written in VBA (Visual Basic for Applications) is often used to compromise the target computers. Macro malware is often obfuscated in several ways to evade detection.
Mamoru Mimura
doaj   +1 more source

GLoW SMOTE-D: Oversampling Technique to Improve Prediction Model Performance of Students Failure in Courses

IEEE Access
The percentage of passing courses is dependent on the assistance provided to students. To ensure the effectiveness of these efforts, identifying students at risk of course failure as early as possible is crucial.
Susana Limanto, Joko Lianto Buliali, Ahmad Saikhu   +2 more
doaj   +1 more source

UkM: A Novel Undersampling Method Using Modified k-Medoids Algorithm

IEEE Access
Learning from imbalanced data remains a persistent challenge in classification tasks, often resulting in biased model performance and poor generalization, particularly for the minority class.
Duygu Selin Turan
doaj   +1 more source

Resampling imbalanced data for network intrusion detection datasets [PDF]

, 2021
Sikha Bagui, Kunqi Li
openalex   +1 more source

An Overview on the Advancements of Support Vector Machine Models in Healthcare Applications: A Review

Information
Support vector machines (SVMs) are well-known machine learning algorithms for classification and regression applications. In the healthcare domain, they have been used for a variety of tasks including diagnosis, prognosis, and prediction of disease ...
Rosita Guido, Stefania Ferrisi, Danilo Lofaro, Domenico Conforti   +3 more
doaj   +1 more source

A Hybrid Approach Handling Imbalanced Datasets [PDF]

, 2009
Several binary classification problems exhibit imbalance in class distribution, influencing system learning. Indeed, traditional machine learning algorithms are biased towards the majority class, thus producing poor predictive accuracy over the minority one. To overcome this limitation, many approaches have been proposed up to now to build artificially
openaire   +1 more source

Anomaly Detection Model for Imbalanced Datasets

, 2020
This paper proposes a method to detect bank frauds using a mixed approach combining a stochastic intensity model with the probability of fraud observed on transactions. It is a dynamic unsupervised approach which is able to predict financial frauds. The fraud prediction probability on the financial transaction is derived as a function of the dynamic ...
Houssou, Régis, Robert-Nicoud, Stephan
openaire   +3 more sources

SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification

IEEE Access
Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority class.
Seema Yadav, Dhruvanshu Joshi, Soham Mulye, Sandeep S. Udmale, Girish P. Bhole   +4 more
doaj   +1 more source