Learning model trees from evolving data streams
By Elena Ikonomovska, João Gama, Saso Dzeroski. 2010
325 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tyler L. Hayes
- Albert Bifet
- Christopher Kanan
- João Gama
- Jerzy Stefanowski
- Indrė Žliobaitė
- Bartosz Krawczyk
- Bernhard Pfahringer
In Signal Processing
- João Gama
- Jerzy Stefanowski
- Albert Bifet
- Indrė Žliobaitė
- G. Holmes
- Bernhard Pfahringer
- Hadi Fanaee-T
- Carlos Eduardo Pedreira
Citations in the corpus
- Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “[37], [38], [39], [56], [63] or ensemble methods [7], [9], [27], [44], [46], [51], [54], [61], [70], [72].”
- Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis (2019, background): “Hoeffding Decision Trees [4, 16, 21, 27, 28] incrementally grow decision trees over time under the Hoeffding bound theoretical guarantees.”
- Event labeling combining ensemble detectors and background knowledge (2014, methodology): “The last algorithm in this table is related to FIMT-DD algorithm [27], which is an online regression tree model.”
- Ensemble learning for data stream analysis: A survey (2017, background): “The most in depth study on learning ensembles of model trees rom data streams appears in [80,81] .”
- Speeding-Up Hoeffding-Based Regression Trees With Options (2011, methodology): “In the second approach by Ikonomovska et al. (2010) the authors propose an incremental algorithm FIMT-DD for any-time model trees learning from evolving data streams with drift detection.”
- Recent advances in decision trees: an updated survey (2022, methodology): “Warm-start is also used in FIMT-DD (Ikonomovska et al. 2011), an online algorithm for inducing model trees capable of dealing with concept drift.”
- Active Learning With Drifting Streaming Data (2014, methodology): “Inspired by [23], we constructed an Airlines dataset using the raw data from U.”
- Explainable Reinforcement Learning: A Survey (2020, methodology): “The LMUT method is compared to five other tree methods: a CART regression tree [40], M5 trees [48] with regression tree options (M5-RT) and with model tree options (M5-MT), and Fast Incremental Model Trees (FIMT, [25]) in the basic version, and in the advanced version with adaptive filters (FIMT-AF)”