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Decision tree-based feature ranking in concept drifting data streams

Authors:

Jean Antonio Pereira Karax,

Andreia Malucelli,

Jean Paul BarddalAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 590 - 592

https://doi.org/10.1145/3297280.3297551

Published: 08 April 2019 Publication History

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Abstract

Data stream mining targets the learning of predictive models that evolve over time according to changes in arriving data. Throughout the years, several approaches have been tailored to create and continuously update predictive models from these streams, and from these, Hoeffding Trees became a popular choice for learning decision trees from data streams. In this paper, we aim at quantifying and expressing the importance of features in dynamic scenarios is of the utmost importance as they allow domain experts to back up, or invalidate, a predictive model. Therefore, we propose and assess a positional gain method tailored for for both individual and ensembles of Hoeffding Trees and how these behave in both synthetic and real-world scenarios.

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Cited By

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Osojnik APanov PDžeroski S(2025)iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression and multi-label classificationMachine Learning10.1007/s10994-024-06718-5114:2Online publication date: 27-Jan-2025
https://doi.org/10.1007/s10994-024-06718-5
Osojnik APanov PDžeroski S(2023)iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-target RegressionDiscovery Science10.1007/978-3-031-45275-8_4(48-63)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45275-8_4
Sharshir SElhelow AKabeel AHassanien AKabeel AElhosseini M(2022)Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluidEnvironmental Science and Pollution Research10.1007/s11356-022-21850-229:60(90632-90655)Online publication date: 23-Jul-2022
https://doi.org/10.1007/s11356-022-21850-2
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Index Terms

Decision tree-based feature ranking in concept drifting data streams
1. Computing methodologies
  1. Machine learning

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Published In

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Osojnik APanov PDžeroski S(2025)iSOUP-SymRF: Symbolic feature ranking with random forests in online multi-target regression and multi-label classificationMachine Learning10.1007/s10994-024-06718-5114:2Online publication date: 27-Jan-2025
https://doi.org/10.1007/s10994-024-06718-5
Osojnik APanov PDžeroski S(2023)iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-target RegressionDiscovery Science10.1007/978-3-031-45275-8_4(48-63)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45275-8_4
Sharshir SElhelow AKabeel AHassanien AKabeel AElhosseini M(2022)Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluidEnvironmental Science and Pollution Research10.1007/s11356-022-21850-229:60(90632-90655)Online publication date: 23-Jul-2022
https://doi.org/10.1007/s11356-022-21850-2
Barddal JEnembreck FLoezer LLanzuolo R(2020)Combining Slow and Fast Learning for Improved Credit Scoring2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283453(1149-1154)Online publication date: 11-Oct-2020
https://dl.acm.org/doi/10.1109/SMC42975.2020.9283453
Barddal JLoezer LEnembreck FLanzuolo R(2020)Lessons learned from data stream classification applied to credit scoringExpert Systems with Applications10.1016/j.eswa.2020.113899162(113899)Online publication date: Dec-2020
https://doi.org/10.1016/j.eswa.2020.113899

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An approach of support approximation to discover frequent patterns from concept-drifting data streams based on concept learning

Learning Higher Accuracy Decision Trees from Concept Drifting Data Streams

An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams

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