Jean Paul Barddal

selected publications

1. ICMLA

   (To appear) Fuels Demand Forecasting: Identifying Leading Feature Sets, Prediction Strategy, and Regressors

   Jonas Krause, Alexandre C. A. Beiruth, and Vinicius Mourão Alves Souza Jean Paul Barddal

   In International Conference on Machine Learning and Applications (ICMLA) 2024

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   Fuels are crucial for any country’s development and economy, impacting various sectors such as transportation, industry, and electricity generation. Accurate prediction of monthly fuel demand can improve supply chain management, strategic decision-making, and financial planning for businesses while helping governments develop decarbonization policies and estimate pollutant emissions. This paper explores machine learning models to forecast fossil fuels and biofuel demand 12 months ahead, using univariate time series data representing the historical sales of 27 Brazilian states, one of the world’s leading producers and consumers of fuels. We evaluate different time series feature sets, machine learning regression models, and prediction strategies to address the complexity of fuel sales influenced by factors such as economic conditions and geopolitical events. Our comprehensive evaluation aims to determine an effective setting for predictive models in the fuel domain. Our results show that popular feature extractors for time series, such as Catch22 and TsFresh, cannot improve the original data representation for most forecasting models. Although focused on Brazil, our findings apply to other countries, since the trained models do not rely on external variables, such as micro and macroeconomic indicators.

   @inproceedings{ICMLA_FUEL_PRED:2024,
     author = {Krause, Jonas and Beiruth, Alexandre C. A. and Jean Paul Barddal, Alceu de Souza Britto Jr, Vinicius Mourão Alves Souza},
     title = {(To appear) Fuels Demand Forecasting: Identifying Leading Feature Sets, Prediction Strategy, and Regressors},
     booktitle = {International Conference on Machine Learning and Applications (ICMLA)},
     year = {2024},
   }

2. ICPR

   (To appear) Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models

   Israel A. Laurensi, Alceu Souza Britto Jr., Jean Paul Barddal, and Alessandro Lameiras Koerich

   In International Conference on Pattern Recognition (ICPR) 2024

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   Facial expression recognition is pivotal in machine learning, facilitating various applications. However, convolutional neural networks (CNNs) are often plagued by catastrophic forgetting, impeding their adaptability. The proposed method, emotion-centered generative replay (ECgr), tackles this challenge by integrating synthetic images from generative adversarial networks. Moreover, ECgr incorporates a quality assurance algorithm to ensure the fidelity of generated images. This dual approach enables CNNs to retain past knowledge while learning new tasks, enhancing their performance in emotion recognition. The experimental results on four diverse facial expression datasets demonstrate that incorporating images generated by our pseudo-rehearsal method enhances training on the targeted dataset and the source dataset while making the CNN retain previously learned knowledge.

   @inproceedings{ICPR_CATASTROPHIC:2024,
     author = {Laurensi, Israel A. and de Souza Britto Jr., Alceu and Barddal, Jean Paul and Koerich, Alessandro Lameiras},
     title = {(To appear) Alleviating Catastrophic Forgetting in {Facial} Expression Recognition with Emotion-Centered Models},
     booktitle = {International Conference on Pattern Recognition (ICPR)},
     year = {2024},
   }

3. ICPR

   (To appear) Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams

   Cristiano Mesquita Garcia, Alessandro Lameiras Koerich, Alceu Souza Britto Jr., and Jean Paul Barddal

   In International Conference on Pattern Recognition (ICPR) 2024

   Abs Bib PDF

   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 the rapid generation of such data, the text stream mining setting, which handles sequentially arriving, potentially infinite text streams, is often more suitable than traditional batch learning. While pre-trained language models are commonly employed for their high-quality text vectorization capabilities in streaming contexts, they face challenges adapting to concept drift—the phenomenon where the data distribution changes over time, adversely affecting model performance. Addressing the issue of concept drift, this study explores the efficacy of seven text sampling methods designed to fine-tune language models, thereby mitigating performance degradation selectively. We precisely assess the impact of these methods on fine-tuning the SBERT model using four different loss functions. Our evaluation, focused on Macro F1-score and elapsed time, employs two text stream datasets and an incremental SVM classifier to benchmark performance. Our findings indicate that Softmax loss and Batch All Triplets loss are particularly effective for text stream classification, demonstrating that larger sample sizes correlate with improved macro F1 scores. Notably, our proposed WordPieceToken ratio sampling method significantly enhances performance with the identified loss functions, surpassing baseline results.

   @inproceedings{ICPR_SAMPLING:2024,
     author = {Garcia, Cristiano Mesquita and Koerich, Alessandro Lameiras and de Souza Britto Jr., Alceu and Barddal, Jean Paul},
     title = {(To appear) Improving Sampling Methods for Fine-tuning SentenceBERT in Text Streams},
     booktitle = {International Conference on Pattern Recognition (ICPR)},
     year = {2024},
   }

4. ESWA

   Temporal analysis of drifting hashtags in textual data streams: A graph-based application

   Cristiano Mesquita Garcia, Alceu Souza Britto Jr., and Jean Paul Barddal

   Expert Systems with Applications 2024

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   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 paper, based on a hashtag stream regarding the hashtag #mybodymychoice, we analyze hashtag drifts over time using concepts from graph analysis and textual data streams using the Girvan–Newman method to uncover hashtag communities in annual snapshots between 2018 and 2022. In addition, we offer insights about some correlated hashtags found in the study. Our approach can be useful for monitoring changes over time in opinions and sentiment patterns about an entity on social media. Even though the hashtag #mybodymychoice was initially coupled with women’s rights, abortion, and bodily autonomy, we observe that it suffered drifts during the studied period across topics such as drug legalization, vaccination, political protests, war, and civil rights. The year 2021 was the most significant drifting year, in which the communities detected and their respective sizes suggest that #mybodymychoice had a significant drift to vaccination and Covid-19-related topics.

   @article{ESWA_GARCIA:2024,
     year = {2024},
     publisher = {Elsevier},
     author = {Garcia, Cristiano Mesquita and de Souza Britto Jr., Alceu and Barddal, Jean Paul},
     title = {Temporal analysis of drifting hashtags in textual data streams: A graph-based application},
     journal = {Expert Systems with Applications},
   }

5. APL. SOFT. COMP.

   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 = {Adaptive Learning on Hierarchical Data Streams using Window-weighted Gaussian Probabilities},
     journal = {Applied Soft Computing},
   }