117 citations · 242 across the 21 of their papers we have counts for
22 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…
Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
Daniel Nowak Assis, Jean Paul Barddal, Fabrício Enembreck
Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as base learners, performing perio…
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…
LongKey: Keyphrase Extraction for Long Documents
Jeovane Honorio Alves, Radu State, Cinthia Obladen de Almendra Freitas +1
In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction add…
Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models
Israel A. Laurensi, Alceu de Souza Britto, Jean Paul Barddal +1
Facial expression recognition is a pivotal component in machine learning, facilitating various applications. However, convolutional neural networks (CNNs) are often plagued by cata…
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities
Luciana Trinkaus Menon, Luiz Carlos Ribeiro Neduziak, Jean Paul Barddal +2
The study of human emotions, traditionally a cornerstone in fields like psychology and neuroscience, has been profoundly impacted by the advent of artificial intelligence (AI). Mul…
Methods for Generating Drift in Text Streams
Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1
Systems and individuals produce data continuously. On the Internet, people share their knowledge, sentiments, and opinions, provide reviews about services and products, and so on.…
Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams
Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu de Souza Britto +1
The proliferation of textual data on the Internet presents a unique opportunity for institutions and companies to monitor public opinion about their services and products. Given th…
Temporal Analysis of Drifting Hashtags in Textual Data Streams: A Graph-Based Application
Cristiano M. Garcia, Alceu de Souza Britto, Jean Paul Barddal
Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on similar topics. In this p…
Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review
Cristiano Mesquita Garcia, Ramon Simoes Abilio, Alessandro Lameiras Koerich +2
The society produces textual data online in several ways, e.g., via reviews and social media posts. Therefore, numerous researchers have been working on discovering patterns in tex…
Random Forest Kernel for High-Dimension Low Sample Size Classification
Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal +1
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learnin…
Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis
Jeovane Honório Alves, Horácio A. C. G. Pedroso, Rafael Honorio Venetikides +4
The growing use of digital communication platforms has given rise to various criminal activities, such as grooming and drug dealing, which pose significant challenges to law enforc…
Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems
Andre Gustavo Hochuli, Jean Paul Barddal, Gillian Cezar Palhano +2
Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space…
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concep…
Evaluating k-NN in the Classification of Data Streams with Concept Drift
Roberto Souto Maior de Barros, Silas Garrido Teixeira de Carvalho Santos, Jean Paul Barddal
Data streams are often defined as large amounts of data flowing continuously at high speed. Moreover, these data are likely subject to changes in data distribution, known as concep…
Pattern Spotting and Image Retrieval in Historical Documents using Deep Hashing
Caio da S. Dias, Alceu de S. Britto, Jean P. Barddal +2
This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects obj…
Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition
Bruna Delazeri, Leonardo L. Veras, Alceu de S. Britto +2
This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The ide…
A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets
Paulo Ricardo Lisboa de Almeida, Jeovane Honório Alves, Rafael Stubs Parpinelli +1
Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often…
A Survey on Concept Drift in Process Mining
Denise Maria Vecino Sato, Sheila Cristiana de Freitas, Jean Paul Barddal +1
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systema…
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…
scikit-dyn2sel -- A Dynamic Selection Framework for Data Streams
Lucca Portes Cavalheiro, Jean Paul Barddal, Alceu de Souza Britto +1
Mining data streams is a challenge per se. It must be ready to deal with an enormous amount of data and with problems not present in batch machine learning, such as concept drift.…
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…