Search Results - "Barddal, Jean Paul"

Random forest kernel for high-dimension low sample size classification by Cavalheiro, Lucca Portes, Bernard, Simon, Barddal, Jean Paul, Heutte, Laurent

“…High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing,…”
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Hierarchical classification of data streams: a systematic literature review by Tieppo, Eduardo, Santos, Roger Robson dos, Barddal, Jean Paul, Nievola, Júlio Cesar

“…The classification task usually works with flat and batch learners, assuming problems as stationary and without relations between class labels. Nevertheless,…”
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Regularized and incremental decision trees for data streams by Barddal, Jean Paul, Enembreck, Fabrício

“…Decision trees are a widely used family of methods for learning predictive models from both batch and streaming data. Despite depicting positive results in a…”
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Adaptive Global k-Nearest Neighbors for Hierarchical Classification of Data Streams by Tieppo, Eduardo, Paul Barddal, Jean, Cesar Nievola, Julio

“…Data stream classification differs from batch learning classification methods as data is made available sequentially and may drift over time. Therefore, data…”
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ADADRIFT: An Adaptive Learning Technique for Long-history Stream-based Recommender Systems by Ferreira Jose, Eduardo, Enembreck, Fabricio, Paul Barddal, Jean

“…Adaptive recommender systems are increasingly showing their importance as profiling is a dynamic problem. Their goal is to update recommendation models as new…”
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Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms by Mochinski, Marcos Alberto, Paul Barddal, Jean, Enembreck, Fabricio

“…This paper proposes a hybrid ensemble learning approach that combines statistical and data stream mining algorithms to obtain better forecasting performance in…”
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Naïve Approaches to Deal With Concept Drifts by Lisboa de Almeida, Paulo R., Oliveira, Luiz S., Souza Britto, Alceu de, Paul Barddal, Jean

“…A common problem in machine learning is to find representative real-world labeled datasets to put the methods to test. When developing approaches to deal with…”
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Adaptive random forests for evolving data stream classification by Gomes, Heitor M., Bifet, Albert, Read, Jesse, Barddal, Jean Paul, Enembreck, Fabrício, Pfharinger, Bernhard, Holmes, Geoff, Abdessalem, Talel

“…Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high…”
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A survey on feature drift adaptation: Definition, benchmark, challenges and future directions by Barddal, Jean Paul, Gomes, Heitor Murilo, Enembreck, Fabrício, Pfahringer, Bernhard

“…•This paper provides insights into a nearly neglected type of drift: feature drifts.•Existing works on feature drift detection and adaptation are…”
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Lessons learned from data stream classification applied to credit scoring by Barddal, Jean Paul, Loezer, Lucas, Enembreck, Fabrício, Lanzuolo, Riccardo

“…The financial credibility of a person is a factor used to determine whether a loan should be approved or not, and this is quantified by a ‘credit score,’ which…”
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Temporal analysis of drifting hashtags in textual data streams: A graph-based application by Garcia, Cristiano Mesquita, Britto, Alceu de Souza, Barddal, Jean Paul

“…Initially supported by Twitter, hashtags are now used on several social media platforms. Hashtags are helpful for tagging, tracking, and grouping posts on…”
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An explainable machine learning approach for student dropout prediction by Krüger, João Gabriel Corrêa, Britto, Alceu de Souza, Barddal, Jean Paul

“…School dropout is a relevant socio-economic problem across the globe. Predictive models have been developed to determine the likelihood of students dropping…”
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Merit-guided dynamic feature selection filter for data streams by Barddal, Jean Paul, Enembreck, Fabrício, Gomes, Heitor Murilo, Bifet, Albert, Pfahringer, Bernhard

“…•DISCUSS tracks the discriminative power of features in streams.•DISCUSS is the first dynamic feature selection algorithm for data streams.•DISCUSS shows…”
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Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilities by Tieppo, Eduardo, Nievola, Júlio Cesar, Barddal, Jean Paul

“…The hierarchical data stream classification task addresses challenges in both hierarchical and data stream classification primary areas. In these scenarios,…”
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A systematic review on computer vision-based parking lot management applied on public datasets by Almeida, Paulo Ricardo Lisboa de, Alves, Jeovane Honório, Parpinelli, Rafael Stubs, Barddal, Jean Paul

“…Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such…”
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Boosting decision stumps for dynamic feature selection on data streams by Barddal, Jean Paul, Enembreck, Fabrício, Gomes, Heitor Murilo, Bifet, Albert, Pfahringer, Bernhard

“…Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve…”
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Representation ensemble learning applied to facial expression recognition by Delazeri, Bruna Rossetto, Hochuli, Andre Gustavo, Barddal, Jean Paul, Koerich, Alessandro Lameiras, Britto, Alceu de Souza

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Incremental specialized and specialized-generalized matrix factorization models based on adaptive learning rate optimizers by Viniski, Antônio David, Barddal, Jean Paul, de Souza Britto Jr, Alceu, de Campos, Humberto Vinicius Aparecido

“…•Incremental models are most suitable for cold-start scenarios.•Adaptive learning rate methods are effective in data stream environments.•Learning…”
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A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start by Viniski, Antônio David, Barddal, Jean Paul, Britto Jr, Alceu de Souza, Enembreck, Fabrício, Campos, Humberto Vinicius Aparecido de

“…•Retail data made available depicts concept drift and cold start problems.•Neural networks are effective in recommending items to supermarket users.•Streaming…”
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Improving Data Stream Classification using Incremental Yeo-Johnson Power Transformation by Tieppo, Eduardo, Barddal, Jean Paul, Nievola, Julio Cesar

“…Data transformation plays an essential role as a preprocessing step in learning models. Several classification techniques have premises about the underlying…”
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