374 citations · 1k across the 46 of their papers we have counts for
47 papers
Concept Drift from a Causal Perspective
Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1
Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model performance. Most existing defini…
Instance Hardness-Based Relevance for Imbalanced Regression
Vitor M. Leitao, Juscimara G. Avelino, George D. C. Cavalcanti +1
Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset. Traditional approaches for…
Multi-stage Dynamic Selection for Cross-Project Defect Prediction
Juscimara G. Avelino, Juscelino S. A. Junior, George D. C. Cavalcanti +1
Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from the target project. However, tr…
A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
Kecia G. de Moura, Robert Sabourin, Rafael M. O. Cruz
Offline handwritten signature verification aims to distinguish genuine from forged signatures using static images. Since real forgeries are rarely available, negative samples are u…
Online K-d tree for approximate neighborhood search in data streams
Eduardo V. L. Barboza, Robert Sabourin, Rafael M. O. Cruz
The k-Nearest Neighbors (kNN) algorithm has long been widely used in Machine Learning (ML) applications. However, the main concern when using it is the computational cost required…
CaDrift: A Time-dependent Causal Generator of Drifting Data Streams
Eduardo V. L. Barboza, Jean Paul Barddal, Robert Sabourin +1
This work presents Causal Drift Generator (CaDrift), a time-dependent synthetic data generator framework based on Structural Causal Models (SCMs). The framework produces a virtuall…
DRES: Fake news detection by dynamic representation and ensemble selection
Faramarz Farhangian, Leandro A. Ensina, George D. C. Cavalcanti +1
The rapid spread of information via social media has made text-based fake news detection critically important due to its societal impact. This paper presents a novel detection meth…
PIPES: A Meta-dataset of Machine Learning Pipelines
Cynthia Moreira Maia, Lucas B. V. de Amorim, George D. C. Cavalcanti +1
Solutions to the Algorithm Selection Problem (ASP) in machine learning face the challenge of high computational costs associated with evaluating various algorithms' performances on…
HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble
Sara B. Coutinho, Rafael M. O. Cruz, Francimaria R. S. Nascimento +1
Psychological biases, such as confirmation bias, make individuals particularly vulnerable to believing and spreading fake news on social media, leading to significant consequences…
IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift
Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto +2
Data streams pose challenges not usually encountered in batch-based ML. One of them is concept drift, which is characterized by the change in data distribution over time. Among man…
Resampling strategies for imbalanced regression: a survey and empirical analysis
Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue…
Imbalanced Regression Pipeline Recommendation
Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks d…
Multi-view autoencoders for Fake News Detection
Ingryd V. S. T. Pereira, George D. C. Cavalcanti, Rafael M. O. Cruz
Given the volume and speed at which fake news spreads across social media, automatic fake news detection has become a highly important task. However, this task presents several cha…
Imbalanced malware classification: an approach based on dynamic classifier selection
J. V. S. Souza, C. B. Vieira, G. D. C. Cavalcanti +1
In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in dev…
Offline Handwritten Signature Verification Using a Stream-Based Approach
Kecia G. de Moura, Rafael M. O. Cruz, Robert Sabourin
Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining…
A post-selection algorithm for improving dynamic ensemble selection methods
Paulo R. G. Cordeiro, George D. C. Cavalcanti, Rafael M. O. Cruz
Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the pro…
Distance Functions and Normalization Under Stream Scenarios
Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto +1
Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not kn…
Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification
Mahdi Alehdaghi, Arthur Josi, Pourya Shamsolmoali +2
Visible-infrared person re-identification seeks to retrieve images of the same individual captured over a distributed network of RGB and IR sensors. Several V-I ReID approaches dir…
Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data
Arthur Josi, Mahdi Alehdaghi, Rafael M. O. Cruz +1
Visible-infrared person re-identification (V-I ReID) seeks to match images of individuals captured over a distributed network of RGB and IR cameras. The task is challenging due to…
The choice of scaling technique matters for classification performance
Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz
Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all v…
Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data
Arthur Josi, Mahdi Alehdaghi, Rafael M. O. Cruz +1
The re-identification (ReID) of individuals over a complex network of cameras is a challenging task, especially under real-world surveillance conditions. Several deep learning mode…
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information
Mahdi Alehdaghi, Arthur Josi, Rafael M. O. Cruz +1
Visible-infrared person re-identification (ReID) aims to recognize a same person of interest across a network of RGB and IR cameras. Some deep learning (DL) models have directly in…
Local overlap reduction procedure for dynamic ensemble selection
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti +1
Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach…
Dynamic Ensemble Selection Using Fuzzy Hyperboxes
Reza Davtalab, Rafael M. O. Cruz, Robert Sabourin
Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query samp…