NDLI: Generalized Cluster Based Fuzzy Model Tree for data modeling and prediction

Content Provider	IEEE Xplore Digital Library
Author	Jin-Il Park Young-Im Cho Myung-Geun Chun
Copyright Year	2010
Description	Author affiliation: Department of Computer Science, Suwon University, Korea (Young-Im Cho) \|\| Department of Electrical and Computer Engineering, Chungbuk National University, Korea (Jin-Il Park; Myung-Geun Chun)
Abstract	In this paper, we propose a novel tree based modeling method, Generalized Cluster based Fuzzy Model Tree (G-CFMT) which can model piecewise linear or piecewise nonlinear dataset and predict a continuous output value. To construct the G-CFMT, data cluster centers are calculated by fuzzy clustering and Extreme Learning Machine (ELM) are obtained at the tree nodes. Since the fuzzy clustering method can render the granulation of dataset, the complexity of the constructed tree is usually low. Moreover, we show that the ELM based scheme can also produce a linear regression model. In the prediction step, fuzzy membership values are calculated from the distance between input data and all cluster centers, the passing nodes from root to the leaf node. Final data prediction is performed by fusing the intermediate induction results, which renders capability of overcoming over-fitting problem of deteriorating the performance for testing data. To validate the proposed method, we have applied our method to various real world datasets. The experimental results clearly underline better performance over other conventional linear and nonlinear modeling and prediction methods in terms of several performance indices.
Starting Page	1
Ending Page	8
File Size	1156974
Page Count	8
File Format	PDF
ISBN	9781424469192
ISSN	10987584
e-ISBN	9781424469215
DOI	10.1109/FUZZY.2010.5584749
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Data models Computational modeling Neurons Training Regression tree analysis Prototypes
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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