173 citations · 257 across the 7 of their papers we have counts for
7 papers
A Comprehensive Study of Machine Learning Techniques for Log-Based Anomaly Detection
Shan Ali, Chaima Boufaied, Domenico Bianculli +2
Growth in system complexity increases the need for automated log analysis techniques, such as Log-based Anomaly Detection (LAD). While deep learning (DL) methods have been widely u…
Measuring Improvement of F-Scores in Detection of Self-Admitted Technical Debt
William Aiken, Paul K. Mvula, Paula Branco +3
Artificial Intelligence and Machine Learning have witnessed rapid, significant improvements in Natural Language Processing (NLP) tasks. Utilizing Deep Learning, researchers have ta…
Automated Imbalanced Classification via Layered Learning
Vitor Cerqueira, Luis Torgo, Paula Branco +1
In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach t…
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
Evan Crothers, Nathalie Japkowicz, Herna Viktor +1
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, whic…
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms
Liyan Chang, Paula Branco
The high volume of increasingly sophisticated cyber threats is drawing growing attention to cybersecurity, where many challenges remain unresolved. Namely, for intrusion detection,…
UBL: an R package for Utility-based Learning
Paula Branco, Rita P. Ribeiro, Luis Torgo
This document describes the R package UBL that allows the use of several methods for handling utility-based learning problems. Classification and regression problems that assume no…
A Survey of Predictive Modelling under Imbalanced Distributions
Paula Branco, Luis Torgo, Rita Ribeiro
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least co…