The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pemilihan ciri adalah bahagian penting dalam mana-mana sistem pengecaman corak. Pengalihan keluar ciri berlebihan meningkatkan kecekapan pengelas serta mengurangkan kos pengekstrakan ciri masa hadapan. Baru-baru ini pengelas rangkaian saraf telah menjadi sangat popular berbanding dengan rakan sejawatannya dari teori statistik. Beberapa kerja mengenai penggunaan rangkaian saraf tiruan sebagai pemilih ciri telah pun dilaporkan. Dalam kerja ini algoritma pemilihan ciri mudah telah dicadangkan di mana rangkaian saraf fraktal, versi diubah suai perceptron berbilang lapisan, telah digunakan sebagai pemilih ciri. Eksperimen telah dilakukan dengan set data IRIS dan SONAR melalui simulasi. Keputusan menunjukkan bahawa algoritma dengan seni bina rangkaian fraktal berfungsi dengan baik untuk mengalih keluar maklumat berlebihan seperti yang diuji oleh kadar pengelasan. Rangkaian saraf fraktal mengambil masa latihan yang lebih sedikit daripada perceptron berbilang lapisan konvensional untuk ketersambungannya yang lebih rendah manakala prestasinya adalah setanding dengan perceptron berbilang lapisan. Kemudahan pelaksanaan perkakasan juga merupakan titik menarik dalam mereka bentuk pemilih ciri dengan rangkaian saraf fraktal.
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Salinan
Basabi CHAKRABORTY, Yasuji SAWADA, "Fractal Neural Network Feature Selector for Automatic Pattern Recognition System" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 9, pp. 1845-1850, September 1999, doi: .
Abstract: Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_9_1845/_p
Salinan
@ARTICLE{e82-a_9_1845,
author={Basabi CHAKRABORTY, Yasuji SAWADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fractal Neural Network Feature Selector for Automatic Pattern Recognition System},
year={1999},
volume={E82-A},
number={9},
pages={1845-1850},
abstract={Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - Fractal Neural Network Feature Selector for Automatic Pattern Recognition System
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1845
EP - 1850
AU - Basabi CHAKRABORTY
AU - Yasuji SAWADA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1999
AB - Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.
ER -