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Polynomial Order Prediction Using a Classifier Trained on Meta-Measurements

Gherman, Bogdan G. and Sirlantzis, Konstantinos (2013) Polynomial Order Prediction Using a Classifier Trained on Meta-Measurements. In: 2013 Fourth International Conference on Emerging Security Technologies. IEEE, pp. 117-120. E-ISBN 978-0-7695-5077-0. (doi:10.1109/EST.2013.26) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/EST.2013.26

Abstract

Polynomial regression is still widely used in engineering and economics where polynomials of low order (usually less than tenth order) are being fitted to experimental data. However, the fundamental problem of selecting the optimal order of the polynomial to be fitted to experimental data is not a straightforward problem. This paper investigates the performance of automated methods for predicting the order of the polynomial that can be fitted on the decision boundary formed between two classes in a pattern recognition problem. We have investigated statistical methods and proposed a method of predicting the order of the polynomial. Our proposed machine learning method is computing a number of measurements on the input data which are used by a classifier trained offline to predict the order of the polynomial that should be fitted to the decision boundary. We have considered two matching scenarios. One scenario is where we have counted only the exact matches as being correct and another scenario in which we count as correct an exact match and higher polynomial orders. Experimental results on synthetic data show that our proposed method predicts the exact order of the polynomial with 31.90% accuracy as opposed to 13.22% of the best statistical method, but it also under-estimates the true order almost twice as often when compared to statistical methods of predicting the order of the polynomial to be fitted to the same data points.

Item Type: Book section
DOI/Identification number: 10.1109/EST.2013.26
Uncontrolled keywords: data points; decision boundary; machine learning method; meta-measurements; model selection; pattern recognition problem; polynomial order prediction; polynomial regression; statistical methods; synthetic data
Subjects: T Technology > TJ Mechanical engineering and machinery > Intelligent control systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Konstantinos Sirlantzis
Date Deposited: 14 Dec 2015 01:35 UTC
Last Modified: 18 Sep 2019 09:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53308 (The current URI for this page, for reference purposes)
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