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2009 Ninth International Conference on Intelligent Systems Design and Applications
A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule
Pisa, Italy
November 30-December 02
ISBN: 978-0-7695-3872-3
ASCII Text
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Valentina Colla, Nicola Matarese, Leonardo M. Reyneri, "A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule," Intelligent Systems Design and Applications, International Conference on, pp. 1307-1311, 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009.
 
 
BibTex
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@article{ 10.1109/ISDA.2009.202,
author = {Valentina Colla and Nicola Matarese and Leonardo M. Reyneri},
title = {A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {0},
year = {2009},
isbn = {978-0-7695-3872-3},
pages = {1307-1311},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2009.202},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule
SN - 978-0-7695-3872-3
SP1307
EP1311
A1 - Valentina Colla,
A1 - Nicola Matarese,
A1 - Leonardo M. Reyneri,
PY - 2009
KW - Data preprocessing
KW - Filtering technique
KW - Distance evaluation
VL - 0
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
When designing a neural or fuzzy system, a careful preprocessing of the database is of utmost importance in order to produce a trustable system. In function approximation applications, when a functional relationship between input and output variables is supposed to exist, the presence of data where the similar set of input variables is associated to very different values of the output is not always beneficial for the final system to design. A method is presented which can be used to detect anomalous data, namely non-coherent associations between input and output patterns. This technique, by mean of a comparison between two distance matrix associated to the input and output patterns, is able to detect elements in a dataset, where similar values of input variables are associated to quite different output values. A numerical example and a more complex application in the pre-processing of data coming from an industrial database were presented.
Index Terms:
Data preprocessing, Filtering technique, Distance evaluation
Citation:
Valentina Colla, Nicola Matarese, Leonardo M. Reyneri, "A Method to Point Out Anomalous Input-Output Patterns in a Database for Training Neuro-Fuzzy System with a Supervised Learning Rule," isda, pp.1307-1311, 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009
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