Jean Paul Barddal

selected publications

1. IJCNN

   (To appear) Benchmarking Feature Extraction Techniques for Textual Data Stream Classification

   Bruno Siedekum Thuma, Pedro Silva Vargas, Cristiano Garcia, Alceu Souza Britto Jr., and Jean Paul Barddal

   In 2023 International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, 2023 2023

   Abs Bib PDF

   Feature extraction regards transforming unstructured or semi-structured data into structured data that can be used as input for classification and sentiment analysis algorithms, among other applications. This task becomes even more challenging and relevant when textual data becomes available over time as a continuous data stream since the lexicon and semantics can be ever-evolving. Data streams are, by definition, potentially infinite sequences of data that may have ephemeral characteristics, that is, where the data behavior changes, it leads to a phenomenon named concept drift. Textual data streams are specialized data streams, in which texts arrive over time from a continual data source, such as social media, raising challenges in which feature extractors are of great help. In this paper, we benchmark different feature extraction algorithms, i.e., Hashing Trick, Word2Vec, BERT, and Incremental Word-Vectors; in textual data stream classification, considering different stream lengths. The evaluation was performed over a binary and a multiclass classification task, considering two different datasets. Results show that pre-trained models, such as BERT, achieve interesting results, while Hashing Trick also performs competitively. We also observe that incremental methods such as Word2Vec and Incremental Word-Vectors are the most prepared for changing scenarios, yet, they are much more computationally intensive compared to the former when applied to larger streams.

   @inproceedings{IJCNN_THUMA:2023,
     author = {Thuma, Bruno Siedekum and de Vargas, Pedro Silva and Garcia, Cristiano and de Souza Britto Jr., Alceu and Barddal, Jean Paul},
     title = {(To appear) Benchmarking Feature Extraction Techniques for Textual Data Stream Classification},
     booktitle = {2023 International Joint Conference on Neural Networks, {IJCNN} 2023,
                    Gold Coast, Australia, 2023},
     year = {2023},
   }

2. IJCNN

   (To appear) Mass-Based Short Term Selection of Classifiers in Data Streams

   Daniel Nowak Assis, Fabricio Enembreck, and Jean Paul Barddal

   In 2023 International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, 2023 2023

   Abs Bib PDF

   Dynamic classifier selection (DCS) regards well-known machine learning techniques in the batch setting that leverage ensemble performance. Most of the methods use similarity-based methods as a proxy, culminating in high computation costs and becoming unfeasible in many streaming scenarios. In this paper, we propose a DCS method able to cope with the high-speed streaming setting, which is based on the performance of base learners in the most recent instances. The impact of our method is evaluated with different ensembles for data streams. We also propose modifications to an Online Boosting method, which has its performance improved with DCS. Our method increases the accuracy and kappa statistic of state-of-the-art ensembles with low overhead of time processing and memory.

   @inproceedings{IJCNN_NOWAK:2023,
     author = {Assis, Daniel Nowak and Enembreck, Fabricio and Barddal, Jean Paul},
     title = {(To appear) Mass-Based Short Term Selection of Classifiers in Data Streams},
     booktitle = {2023 International Joint Conference on Neural Networks, {IJCNN} 2023,
                    Gold Coast, Australia, 2023},
     year = {2023},
   }

3. INFUS

   Exploring diversity in data complexity and classifier decision spaces for pool generation

   Marcos Monteiro, Alceu S. Britto, Jean P. Barddal, Luiz S. Oliveira, and Robert Sabourin

   Information Fusion 2023

   Abs Bib PDF

   This paper introduces a novel method for classifier pool generation in which a two-level strategy explores diversity in both data complexity and classifier decision spaces. The rationale is to induce pool members using data subsets representing subproblems with different difficulties while promoting diversity in classifiers’ decisions. Two possible variants of the proposed method with a focus on maximum dispersion and maximum accuracy are presented. These differ in the property used to define the best pool of classifiers provided by an optimization process. A robust experimental protocol encompassing 28 classification datasets shows that the proposed pool generation provided the best accuracy on 327 over 336 experiments (97.3%) when compared to well-known pool generation methods to provide multiple classifier systems with and without dynamic selection.

   @article{INFUS_MARCOS_MONTEIRO:2022,
     title = {Exploring diversity in data complexity and classifier decision spaces for pool generation},
     journal = {Information Fusion},
     volume = {89},
     pages = {567-587},
     year = {2023},
     issn = {1566-2535},
     doi = {https://doi.org/10.1016/j.inffus.2022.09.001},
     url = {https://www.sciencedirect.com/science/article/pii/S1566253522001336},
     author = {Monteiro, Marcos and Britto, Alceu S. and Barddal, Jean P. and Oliveira, Luiz S. and Sabourin, Robert},
     keywords = {Classifier pool generation, Diversity, Data complexity measures},
   }