Jean Paul Barddal

selected publications

1. APL. SOFT. COMP.

   (To Appear) Adaptive Learning on Hierarchical Data Streams using Window-weighted Gaussian Probabilities

   Eduardo Tieppo, Julio Cesar Nievola, and Jean Paul Barddal

   Applied Soft Computing 2024

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   The hierarchical data stream classification task addresses challenges in both hierarchical and data stream classification primary areas. In these scenarios, machine learning models must simultaneously deal with class hierarchies and adapt to respond to nonstationary data. Given such a challenging set of traits, existing techniques are deficient, as they perform incremental learning and are slow to adapt to newer data, thus not capturing their dynamics in a timely fashion. In this study, we propose two novel adaptive Gaussian Naive Bayes classifiers tailored to classify hierarchical data streams. The models use window-weighted Gaussian probabilities to consider current and historical data and improve the adaptability of the classifiers, especially for nonstationary data streams. As a result of our research, we introduce a unified protocol for evaluating and comparing hierarchical data stream classifiers and establish a benchmark for the hierarchical data stream classification task encompassing the proposed methods and state-of-the-art classifiers. The results demonstrate that our proposed algorithms achieve better prediction correctness than their state-of-the-art counterparts while responding more swiftly to changes in data distribution.

   @article{ASOC_TIEPPO:2023,
     year = {2024},
     publisher = {Elsevier},
     author = {Tieppo, Eduardo and Nievola, Julio Cesar and Barddal, Jean Paul},
     title = {(To Appear) Adaptive Learning on Hierarchical Data Streams using Window-weighted Gaussian Probabilities},
     journal = {Applied Soft Computing},
   }

2. STATS. & COMP.

   (To Appear) Random Forest Kernel for High-Dimension Low Sample Size Classification

   Lucca Portes Cavalheiro, Simon Bernard, Jean Paul Barddal, and Laurent Heutte

   Statistics and Computing 2023

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   High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context. Experiments conducted on 40 public HDLSS classification datasets, supported by rigorous statistical analyses, show that the RFSVM method outperforms existing methods for the majority of HDLSS problems and remains at the same time very competitive for low or non-HDLSS problems.

   @article{STATISTICS_COMPUTING_HDLSS:2023,
     year = {2023},
     publisher = {Springer},
     author = {Cavalheiro, Lucca Portes and Bernard, Simon and Barddal, Jean Paul and Heutte, Laurent},
     title = {(To Appear) Random Forest Kernel for High-Dimension Low Sample Size Classification},
     journal = {Statistics and Computing},
   }

3. ICMLA

   (To Appear) 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, Joel E. M. Koster, Luiz Rodrigo Grochocki, Cinthia Obladen Almendra Freitas, and Jean Paul Barddal

   In International Conference on Machine Learning with Applications (ICMLA) 2023

   Abs Bib PDF

   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 enforcement and forensic experts. This paper presents a supervised keyphrase extraction approach to detect relevant information in high-volume chat logs involving grooming and drug dealing for forensic analysis. The proposed method, JointKPE++, builds upon the JointKPE keyphrase extractor by employing improvements to handle longer texts effectively. We evaluate JointKPE++ using BERT-based pre-trained models on grooming and drug dealing datasets, including BERT, RoBERTa, SpanBERT, and BERTimbau. The results show significant improvements over traditional approaches and demonstrate the potential for JointKPE++ to aid forensic experts in efficiently detecting keyphrases related to criminal activities.

   @inproceedings{ALVES_ICMLA:2023,
     author = {Alves, Jeovane Honório and Pedroso, Horácio A. C. G. and Venetikides, Rafael Honorio and Koster, Joel E. M. and Grochocki, Luiz Rodrigo and de Almendra Freitas, Cinthia Obladen and Barddal, Jean Paul},
     title = {(To Appear) Detecting Relevant Information in High-Volume Chat Logs: Keyphrase Extraction for Grooming and Drug Dealing Forensic Analysis},
     booktitle = {International Conference on Machine Learning with Applications (ICMLA)},
     year = {2023},
   }

4. NEUCOM

   (To Appear) Incremental Specialized and Specialized-Generalized Matrix Factorization Models based on Adaptive Learning Rate Optimizers

   Antônio David Viniski, Jean Paul Barddal, and Alceu Souza Britto Jr.

   Neurocomputing 2023

   Abs Bib PDF

   Recommender systems suggest items that are likely to be preferred by a particular user based on historical behavior, actions, and feedback. In real-world applications, data on users and items are continuously generated at a fast pace, such as in e-commerce, social media, digital marketing, and content consumption applications. Since interactions occur over time, these scenarios can be formulated as a data stream where users’ interests are potentially dynamic, i.e., they change over time. Given that changes are expected to occur, one of the current research challenges in streaming recommender systems is that models must adapt their parameters when changes occur to maintain performance. As such changes do not occur for all users and items in the stream at the same time, we consider adapting learning schemes to account for user or item identifiers and model individual parameters. Therefore, we used specialized parameters to adjust the step size for each dataset user or item. More specifically, this study proposes four specialized and specialized-generalized variants of four well-known adaptive learning rate optimizers and shows how they are combined with incremental matrix factorization methods. We tested our proposed optimization strategies on different datasets and showed that one of the proposed specialized variants, that is, InAMSGradUser, improves the RECALL and NDCG rates by up to 11.1 and 7.5 percentage points, respectively, compared to the traditional stochastic gradient descent (SGD) optimizer.

   @article{NEUCOM_VINISKI:2023,
     year = {2023},
     publisher = {Elsevier},
     author = {Viniski, Antônio David and Barddal, Jean Paul and de Souza Britto Jr., Alceu},
     title = {(To Appear) Incremental Specialized and Specialized-Generalized Matrix Factorization Models based on Adaptive Learning Rate Optimizers},
     journal = {Neurocomputing},
   }