NDLI: Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Content Provider	SpringerLink
Author	Mirzamomen, Zahra Kangavari, Mohammad Reza
Copyright Year	2016
Abstract	Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.
Starting Page	341
Ending Page	363
Page Count	23
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	45
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2016-06-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Pattern recognition Data stream classification Decision tree Min–max neural network Stability Artificial Intelligence (incl. Robotics) Complex Systems Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Artificial Intelligence Software Neuroscience

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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