Search Results for author: Albert Bifet

Found 41 papers, 19 papers with code

Branches: A Fast Dynamic Programming and Branch & Bound Algorithm for Optimal Decision Trees

1 code implementation4 Jun 2024 Ayman Chaouki, Jesse Read, Albert Bifet

Algorithms like DL8. 5 and MurTree operate on the space of nodes (or branches), they are very fast, but do not penalise complex Decision Trees, i. e. they do not solve for sparsity.

Interpretable Machine Learning

Online Learning of Decision Trees with Thompson Sampling

1 code implementation9 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.

Interpretable Machine Learning Thompson Sampling

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning

1 code implementation30 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.

Anomaly Detection Class Incremental Learning +1

Preventing Discriminatory Decision-making in Evolving Data Streams

no code implementations16 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.

Decision Making Fairness

A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal

1 code implementation28 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.

Continual Learning Reinforcement Learning (RL)

Linear TreeShap

1 code implementation16 Sep 2022 Peng Yu, Chao Xu, Albert Bifet, Jesse Read

Decision trees are well-known due to their ease of interpretability.

Green Accelerated Hoeffding Tree

no code implementations6 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%.

Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021

no code implementations12 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.

Recommendation Systems

Improving the performance of bagging ensembles for data streams through mini-batching

no code implementations18 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.

Ensemble Learning Incremental Learning

Closed-loop Control for Online Continual Learning

no code implementations29 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.

Continual Learning

Sketches for Time-Dependent Machine Learning

2 code implementations26 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.

BIG-bench Machine Learning Time Series +1

FARF: A Fair and Adaptive Random Forests Classifier

no code implementations17 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.

Decision Making Fairness

A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

no code implementations16 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.

Active Learning Benchmarking

Model Compression for Dynamic Forecast Combination

1 code implementation5 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.

Model Compression Time Series +1

A Survey on Spatio-temporal Data Analytics Systems

1 code implementation17 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.

STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection

1 code implementation1 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.

Machine Learning (In) Security: A Stream of Problems

no code implementations30 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.

BIG-bench Machine Learning

An Eager Splitting Strategy for Online Decision Trees

no code implementations20 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.

Emergent and Unspecified Behaviors in Streaming Decision Trees

no code implementations16 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.

CURIE: A Cellular Automaton for Concept Drift Detection

1 code implementation21 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.

Adaptive XGBoost for Evolving Data Streams

1 code implementation15 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.

General Classification

Rebalancing Learning on Evolving Data Streams

1 code implementation17 Nov 2019 Alessio Bernardo, Emanuele Della Valle, Albert Bifet

For this reason we propose a new streaming approach able to rebalance data streams online.

BIG-bench Machine Learning

Spiking Neural Networks and Online Learning: An Overview and Perspectives

no code implementations23 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.

Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning

no code implementations23 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.

Recurring Concept Meta-learning for Evolving Data Streams

no code implementations21 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.

Diversity General Classification +1

Resource-aware Elastic Swap Random Forest for Evolving Data Streams

1 code implementation14 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.

Continual Learning

Scikit-Multiflow: A Multi-output Streaming Framework

1 code implementation12 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.

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

no code implementations12 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.

BIG-bench Machine Learning

VHT: Vertical Hoeffding Tree

no code implementations28 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.

BIG-bench Machine Learning

Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams

no code implementations23 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.

General Classification

Kaggle LSHTC4 Winning Solution

no code implementations3 May 2014 Antti Puurula, Jesse Read, Albert Bifet

The number of documents per label is chosen using label priors and thresholding of vote scores.

text-classification Text Classification

Learning from Time-Changing Data with Adaptive Windowing

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.

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