387 citations · 1.6k across the 48 of their papers we have counts for
52 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…
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…
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…
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…
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…
Classifier Pool Generation based on a Two-level Diversity Approach
Marcos Monteiro, Alceu S. Britto, Jean P. Barddal +2
This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measur…
A Comprehensive Comparison of End-to-End Approaches for Handwritten Digit String Recognition
Andre G. Hochuli, Alceu S. Britto, David A. Saji +3
Over the last decades, most approaches proposed for handwritten digit string recognition (HDSR) have resorted to digit segmentation, which is dominated by heuristics, thereby impos…
An Investigation of Feature Selection and Transfer Learning for Writer-Independent Offline Handwritten Signature Verification
Victor L. F. Souza, Adriano L. I. Oliveira, Rafael M. O. Cruz +1
SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network…
Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation
Teruo M. Maruyama, Luiz S. Oliveira, Alceu S. Britto +1
Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of sig…
Random Forest for Dissimilarity-based Multi-view Learning
Simon Bernard, Hongliu Cao, Robert Sabourin +1
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies ar…
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Hongliu Cao, Simon Bernard, Robert Sabourin +1
Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between t…
Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control
Victor L. F. Souza, Adriano L. I. Oliveira, Rafael M. O. Cruz +1
This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Veri…
A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification
Victor L. F. Souza, Adriano L. I. Oliveira, Rafael M. O. Cruz +1
High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and diffic…
Multi-label learning for dynamic model type recommendation
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti +1
Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local…
An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings
Andre G. Hochuli, Alceu S. Britto, Jean P. Barddal +2
An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognize…
Meta-learning for fast classifier adaptation to new users of Signature Verification systems
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifie…
A Decision-Based Dynamic Ensemble Selection Method for Concept Drift
Regis Antonio Saraiva Albuquerque, Albert Franca Josua Costa, Eulanda Miranda dos Santos +2
We propose an online method for concept driftdetection based on dynamic classifier ensemble selection. Theproposed method generates a pool of ensembles by promotingdiversity among…
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning syst…
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Rafael M. O. Cruz, Mariana A. Souza, Robert Sabourin +1
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention…
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira +3
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implication…
On Meta-Learning for Dynamic Ensemble Selection
Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is…