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A Generic Feature Extraction Model using Learnable Evolution Models (LEM+ID3)

by M. Elemam Shehab, K. Badran, Gouda I. Salama
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 11
Year of Publication: 2013
Authors: M. Elemam Shehab, K. Badran, Gouda I. Salama
10.5120/10680-5562

M. Elemam Shehab, K. Badran, Gouda I. Salama . A Generic Feature Extraction Model using Learnable Evolution Models (LEM+ID3). International Journal of Computer Applications. 64, 11 ( February 2013), 27-32. DOI=10.5120/10680-5562

@article{ 10.5120/10680-5562,
author = { M. Elemam Shehab, K. Badran, Gouda I. Salama },
title = { A Generic Feature Extraction Model using Learnable Evolution Models (LEM+ID3) },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 11 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number11/10680-5562/ },
doi = { 10.5120/10680-5562 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:16:09.087500+05:30
%A M. Elemam Shehab
%A K. Badran
%A Gouda I. Salama
%T A Generic Feature Extraction Model using Learnable Evolution Models (LEM+ID3)
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 11
%P 27-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Inspired originally by the Learnable Evolution Model(LEM) a new presents of new classification algorithm called (LEM+ID3), which is based on the techniques from the learnable evolution models (LEM) to enhance convergence and accuracy of the algorithm and use of ID3 in order to construct the tree used in classification. In this paper a new version of LEM which convert LEM from optimization domain to classification domain and then examine the feature extraction problems and show that learning evolutional can significantly enhance the performance of pattern recognition systems with simple classifiers. This model is applied to real world datasets from the UCI Machine Learning databases to verify proposed approach and compare it with other convention classifiers. The conclusion is this algorithm is able to produce classifiers of superior (or equivalent) performance to the conventional classifiers examined Also time taken to reach near optimum accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Pattern Recognition Learnable Evolution Model Dynamic Threshold Classifier

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