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Soft Computing

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18-06-2015 | Methodologies and Application

MFlexDT: multi flexible fuzzy decision tree for data stream classification

Authors: Ayaz Isazadeh, Farnaz Mahan, Witold Pedrycz

Published in: Soft Computing | Issue 9/2016

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Abstract

In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

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Title: MFlexDT: multi flexible fuzzy decision tree for data stream classification
Authors: Ayaz Isazadeh
Farnaz Mahan
Witold Pedrycz
Publication date: 18-06-2015
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 9/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-015-1733-2

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