no code implementations • 9 Apr 2024 • Ayman Chaouki, Jesse Read, Albert Bifet
Recent breakthroughs addressed this suboptimality issue in the batch setting, but no such work has considered the online setting with data arriving in a stream.
no code implementations • 16 Jan 2024 • Jakub Białek, Wojtek Kuberski, Nikolaos Perrakis, Albert Bifet
To address this, we introduce a new method for evaluating classification models on unlabeled data that accurately quantifies the impact of covariate shift on model performance and call it Probabilistic Adaptive Performance Estimation (PAPE).
1 code implementation • 30 Oct 2023 • Anton Lee, Yaqian Zhang, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer
A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference.
no code implementations • 18 May 2023 • Nedeljko Radulovic, Albert Bifet, Fabian Suchanek
BELLA provides explanations in the form of a linear model trained in the feature space.
no code implementations • 16 Feb 2023 • Zichong Wang, Nripsuta Saxena, Tongjia Yu, Sneha Karki, Tyler Zetty, Israat Haque, Shan Zhou, Dukka Kc, Ian Stockwell, Albert Bifet, Wenbin Zhang
However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.
1 code implementation • 28 Sep 2022 • Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Nick Jin Sean Lim, Yunzhe Jia
Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples.
1 code implementation • 16 Sep 2022 • Peng Yu, Chao Xu, Albert Bifet, Jesse Read
Decision trees are well-known due to their ease of interpretability.
no code implementations • 6 May 2022 • Eva Garcia-Martin, Albert Bifet, Niklas Lavesson, Rikard König, Henrik Linusson
The results show that GAHT is able to achieve the same competitive accuracy results compared to EFDT and ensembles of Hoeffding trees while reducing the energy consumption up to 70%.
1 code implementation • 27 Apr 2022 • Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, Joao Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau
However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time.
no code implementations • 27 Jan 2022 • Thomas Guyet, Wenbin Zhang, Albert Bifet
The need to analyze information from streams arises in a variety of applications.
no code implementations • 17 Jan 2022 • Guilherme Cassales, Heitor Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger
Such strategies can significantly reduce energy consumption in 96% of the experimental scenarios evaluated.
no code implementations • 12 Jan 2022 • João Vinagre, Alípio Mário Jorge, Marie Al-Ghossein, Albert Bifet
This can be overwhelming for systems and algorithms designed to train in batches, given the continuous and potentially fast change of content, context and user preferences or intents.
no code implementations • 18 Dec 2021 • Guilherme Cassales, Heitor Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger
This paper proposes a mini-batching strategy that can improve memory access locality and performance of several ensemble algorithms for stream mining in multi-core environments.
no code implementations • 29 Sep 2021 • Yaqian Zhang, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Jin Sean Lim, Alvin Jia
To address the non-stationarity in the continual learning environment, we employ a Q function with task-specific and task-shared components to support fast adaptation.
2 code implementations • 26 Aug 2021 • Jesus Antonanzas, Marta Arias, Albert Bifet
Time series data can be subject to changes in the underlying process that generates them and, because of these changes, models built on old samples can become obsolete or perform poorly.
no code implementations • 17 Aug 2021 • Wenbin Zhang, Albert Bifet, Xiangliang Zhang, Jeremy C. Weiss, Wolfgang Nejdl
This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance.
no code implementations • 16 Jun 2021 • Heitor Murilo Gomes, Maciej Grzenda, Rodrigo Mello, Jesse Read, Minh Huong Le Nguyen, Albert Bifet
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare.
1 code implementation • 5 Apr 2021 • Vitor Cerqueira, Luis Torgo, Carlos Soares, Albert Bifet
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
1 code implementation • 17 Mar 2021 • Md Mahbub Alam, Luis Torgo, Albert Bifet
Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems.
1 code implementation • 1 Mar 2021 • Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet, Luis Torgo
In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
2 code implementations • 8 Dec 2020 • Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.
no code implementations • 30 Oct 2020 • Fabrício Ceschin, Marcus Botacin, Albert Bifet, Bernhard Pfahringer, Luiz S. Oliveira, Heitor Murilo Gomes, André Grégio
Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field.
no code implementations • 20 Oct 2020 • Chaitanya Manapragada, Heitor M Gomes, Mahsa Salehi, Albert Bifet, Geoffrey I Webb
In this work, we study in ensemble settings the effectiveness of replacing the split strategy for the state-of-the-art online tree learner, Hoeffding Tree, with a rigorous but more eager splitting strategy that we had previously published as Hoeffding AnyTime Tree.
no code implementations • 16 Oct 2020 • Chaitanya Manapragada, Geoffrey I Webb, Mahsa Salehi, Albert Bifet
Hoeffding trees are the state-of-the-art methods in decision tree learning for evolving data streams.
1 code implementation • 21 Sep 2020 • Jesus L. Lobo, Javier Del Ser, Eneko Osaba, Albert Bifet, Francisco Herrera
Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.
1 code implementation • 15 May 2020 • Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet
The proposed method creates new members of the ensemble from mini-batches of data as new data becomes available.
1 code implementation • 17 Nov 2019 • Alessio Bernardo, Emanuele Della Valle, Albert Bifet
For this reason we propose a new streaming approach able to rebalance data streams online.
no code implementations • 23 Jul 2019 • Jesus L. Lobo, Izaskun Oregi, Albert Bifet, Javier Del Ser
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios.
no code implementations • 23 Jul 2019 • Jesus L. Lobo, Javier Del Ser, Albert Bifet, Nikola Kasabov
Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores.
no code implementations • 21 May 2019 • Robert Anderson, Yun Sing Koh, Gillian Dobbie, Albert Bifet
The novelty of ECPF is in how it uses similarity of classifications on new data, between a new classifier and existing classifiers, to quickly identify the best classifier to reuse.
1 code implementation • 14 May 2019 • Diego Marrón, Eduard Ayguadé, José Ramon Herrero, Albert Bifet
This paper presents Elastic Swap Random Forest ({\em ESRF}), a method for reducing the number of trees in the ARF ensemble while providing similar accuracy.
1 code implementation • 12 Jul 2018 • Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem
Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language.
no code implementations • 12 Feb 2018 • Tian Guo, Albert Bifet, Nino Antulov-Fantulin
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market.
no code implementations • 28 Jul 2016 • Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet, Arinto Murdopo
IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed.
no code implementations • 3 Nov 2015 • Diego Marrón, Jesse Read, Albert Bifet, Nacho Navarro
Big Data streams are being generated in a faster, bigger, and more commonplace.
no code implementations • 23 Apr 2015 • Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer
In this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment.
no code implementations • 3 May 2014 • Antti Puurula, Jesse Read, Albert Bifet
The number of documents per label is chosen using label priors and thresholding of vote scores.
2 code implementations • Proceedings of the Seventh SIAM International Conference on Data Mining 2007 • Albert Bifet, Ricard Gavalda
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time.
1 code implementation • Fourth International Workshop on Knowledge Discovery from Data Streams 2006 • Manuel Baena-Garcia, Jose del Campo- Avila, Raul Fidalgo, Albert Bifet, Ricard Gavalda, and Rafael Morales-Bueno
An emerging problem in Data Streams is the detection of concept drift.