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Found 38 papers, 16 papers with code
Online Learning of Decision Trees with Thompson Sampling
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.
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning
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.
BELLA: Black box model Explanations by Local Linear Approximations
BELLA provides explanations in the form of a linear model trained in the feature space.
Preventing Discriminatory Decision-making in Evolving Data Streams
However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples.
Green Accelerated Hoeffding Tree
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%.
Open challenges for Machine Learning based Early Decision-Making research
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.
Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences
The need to analyze information from streams arises in a variety of applications.
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing
Such strategies can significantly reduce energy consumption in 96% of the experimental scenarios evaluated.
Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling -- ORSUM 2021
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.
Improving the performance of bagging ensembles for data streams through mini-batching
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.
Closed-loop Control for Online Continual Learning
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.
Sketches for Time-Dependent Machine Learning
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.
FARF: A Fair and Adaptive Random Forests Classifier
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.
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare.
Model Compression for Dynamic Forecast Combination
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
A Survey on Spatio-temporal Data Analytics Systems
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
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.
River: machine learning for streaming data in Python
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.
Machine Learning (In) Security: A Stream of Problems
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.
An Eager Splitting Strategy for Online Decision Trees
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
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
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
The proposed method creates new members of the ensemble from mini-batches of data as new data becomes available.
Rebalancing Learning on Evolving Data Streams
For this reason we propose a new streaming approach able to rebalance data streams online.
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios.
Spiking Neural Networks and Online Learning: An Overview and Perspectives
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.
Recurring Concept Meta-learning for Evolving Data Streams
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.
Resource-aware Elastic Swap Random Forest for Evolving Data Streams
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.
Scikit-Multiflow: A Multi-output Streaming Framework
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
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.
VHT: Vertical Hoeffding Tree
IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed.
Data Stream Classification using Random Feature Functions and Novel Method Combinations
Big Data streams are being generated in a faster, bigger, and more commonplace.
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams
In this research, we apply ensembles of Fourier encoded spectra to capture and mine recurring concepts in a data stream environment.
Kaggle LSHTC4 Winning Solution
The number of documents per label is chosen using label priors and thresholding of vote scores.
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time.
