Adaptive online incremental learning for evolving data streams

Applied Soft Computing ◽

10.1016/j.asoc.2021.107255 ◽

2021 ◽

Vol 105 ◽

pp. 107255

Author(s):

Si-si Zhang ◽

Jian-wei Liu ◽

Xin Zuo

Keyword(s):

Data Streams ◽

Incremental Learning ◽

Evolving Data ◽

Online Incremental Learning

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Autonomous Deep Learning: Incremental Learning of Deep Neural Networks for Evolving Data Streams

2019 International Conference on Data Mining Workshops (ICDMW) ◽

10.1109/icdmw.2019.00023 ◽

2019 ◽

Cited By ~ 1

Author(s):

Andri Ashfahani

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Data Streams ◽

Incremental Learning ◽

Deep Neural Networks ◽

Evolving Data

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Efficient class incremental learning for multi-label classification of evolving data streams

2014 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2014.6889926 ◽

2014 ◽

Cited By ~ 7

Author(s):

Zhongwei Shi ◽

Yun Xue ◽

Yimin Wen ◽

Guoyong Cai

Keyword(s):

Data Streams ◽

Incremental Learning ◽

Evolving Data

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An Incremental Learning approach using Long Short-Term Memory Neural Networks

10.48011/asba.v2i1.1491 ◽

2020 ◽

Author(s):

Álvaro C. Lemos Neto ◽

Rodrigo A. Coelho ◽

Cristiano L. de Castro

Keyword(s):

Data Streams ◽

Incremental Learning ◽

Memory Management ◽

Short Term Memory ◽

Concept Drift ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Noise Data ◽

Evolving Data ◽

Robust To Noise

Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but most of them have one thing in common: they are complex variants of batch learning algorithms, which is a problem since, in a streaming setting, less complexity and more performance is desired. This paper proposes the Incremental LSTM model, which is a variant of the original LSTM with minor changes, that can tackle evolving data streams problems such as concept drift and the elasticity-plasticity dilemma without neither needing a dedicated drift detector nor a memory management system. It obtained great results that show it reacts fast to concept drifts and that is also robust to noise data.

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