Incremental Learning of Fuzzy Decision Trees for Streaming Data Classification

DOI

10.2991/eusflat-19.2019.102How to use a DOI?

Keywords

Streaming Data Classification Hoeffding Decision Tree Fuzzy Decision Tree Evolving Classifiers

Abstract

Data stream analysis is growing in popularity in the last years since several application domains require to continuously and quickly analyse data produced by sensors with the aim of, for instance, reacting immediately when problems arise, or detecting new trends. The specificity of these domains imposes strict temporal constraints on machine learning algorithms to be used for mining useful insights. The Hoeffding Decision Tree (HDT) is a well-known classification algorithm for efficient streaming data classification. In this paper, with the aim of improving HDT accuracy and capability of handling noisy data, we exploit the learning procedure proposed in HDT for adapting a recently proposed fuzzy decision tree to cope with streaming data classification problems. We tested the fuzzy approach on a benchmark dataset for the on-line learning of data stream classification models. Results show that, during the on-line learning process, the fuzzy approach outperforms HDT in terms of accuracy.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Riccardo Pecori
AU  - Pietro Ducange
AU  - Francesco Marcelloni
PY  - 2019/08
DA  - 2019/08
TI  - Incremental Learning of Fuzzy Decision Trees for Streaming Data Classification
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 748
EP  - 755
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.102
DO  - 10.2991/eusflat-19.2019.102
ID  - Pecori2019/08
ER  -