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Found 15 papers, 6 papers with code
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time.
Pandemic infection forecasting through compartmental model and learning-based approaches
Motivated by this, we present a hybrid pandemic infection forecasting methodology that integrates compartmental model and learning-based approaches.
A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases
Severe acute respiratory disease SARS-CoV-2 has had a found impact on public health systems and healthcare emergency response especially with respect to making decisions on the most effective measures to be taken at any given time.
Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation
In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas.
Unsupervised Unlearning of Concept Drift with Autoencoders
Consequently, learning models operating on the data stream might become obsolete, and need costly and difficult adjustments such as retraining or adaptation.
Data augmentation on-the-fly and active learning in data stream classification
Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size.
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
In real-world applications, the process generating the data might suffer from nonstationary effects (e. g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour).
Nonstationary data stream classification with online active learning and siamese neural networks
We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels.
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies.
Data-efficient Online Classification with Siamese Networks and Active Learning
In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification.
Online Learning With Adaptive Rebalancing in Nonstationary Environments
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications.
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems
These are learning time, scalability and decentralised coordination i. e. no communication between the learning agents.
Queue-based Resampling for Online Class Imbalance Learning
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift.
Real-Time Bidding by Reinforcement Learning in Display Advertising
In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.
Feature Selection as a Multiagent Coordination Problem
In this paper, we formulate feature selection as a multiagent coordination problem and propose a novel feature selection method using multiagent reinforcement learning.
