Feature selection and chromosome classification using a multilayer perceptron neural network

B. Lerner; M. Levinstein; B. Rosenberg; H. Guterman; I. Dinstein; Y. Romem

doi:10.1109/icnn.1994.374905

Feature selection and chromosome classification using a multilayer perceptron neural network

B. Lerner, M. Levinstein, B. Rosenberg, H. Guterman, I. Dinstein, Y. Romem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

50 Scopus citations

Abstract

Two feature selection techniques and a multilayer perceptron (MLP) neural network (NN) have been used in this study for human chromosome classification. The first technique is the `knock-out' algorithm and the second is the Principal Component Analysis (PCA). The `knock-out' algorithm emphasized the significance of the centrometric index and of the chromosome length, as features in chromosome classification. The PCA technique demonstrated the importance of retaining most of the image information whenever small training sets are used. However, the use of large training sets enables considerable data compression. Both techniques yield the benefit of using only about 70% of the available features to get almost the ultimate classification performance.

Original language	English
Title of host publication	IEEE International Conference on Neural Networks - Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers
Pages	3540-3545
Number of pages	6
ISBN (Print)	078031901X
DOIs	https://doi.org/10.1109/icnn.1994.374905
State	Published - 1 Jan 1994
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 27 Jun 1994 → 29 Jun 1994

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	6

Conference

Conference	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	27/06/94 → 29/06/94

ASJC Scopus subject areas

Software

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10.1109/icnn.1994.374905

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Lerner, B., Levinstein, M., Rosenberg, B., Guterman, H., Dinstein, I., & Romem, Y. (1994). Feature selection and chromosome classification using a multilayer perceptron neural network. In IEEE International Conference on Neural Networks - Conference Proceedings (pp. 3540-3545). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icnn.1994.374905

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title = "Feature selection and chromosome classification using a multilayer perceptron neural network",

abstract = "Two feature selection techniques and a multilayer perceptron (MLP) neural network (NN) have been used in this study for human chromosome classification. The first technique is the `knock-out' algorithm and the second is the Principal Component Analysis (PCA). The `knock-out' algorithm emphasized the significance of the centrometric index and of the chromosome length, as features in chromosome classification. The PCA technique demonstrated the importance of retaining most of the image information whenever small training sets are used. However, the use of large training sets enables considerable data compression. Both techniques yield the benefit of using only about 70% of the available features to get almost the ultimate classification performance.",

author = "B. Lerner and M. Levinstein and B. Rosenberg and H. Guterman and I. Dinstein and Y. Romem",

year = "1994",

month = jan,

day = "1",

doi = "10.1109/icnn.1994.374905",

language = "English",

isbn = "078031901X",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "3540--3545",

booktitle = "IEEE International Conference on Neural Networks - Conference Proceedings",

address = "United States",

note = "Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) ; Conference date: 27-06-1994 Through 29-06-1994",

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Lerner, B, Levinstein, M, Rosenberg, B, Guterman, H , Dinstein, I & Romem, Y 1994, Feature selection and chromosome classification using a multilayer perceptron neural network. in IEEE International Conference on Neural Networks - Conference Proceedings. IEEE International Conference on Neural Networks - Conference Proceedings, vol. 6, Institute of Electrical and Electronics Engineers, pp. 3540-3545, Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, 27/06/94. https://doi.org/10.1109/icnn.1994.374905

Feature selection and chromosome classification using a multilayer perceptron neural network. / Lerner, B.; Levinstein, M.; Rosenberg, B. et al.
IEEE International Conference on Neural Networks - Conference Proceedings. Institute of Electrical and Electronics Engineers, 1994. p. 3540-3545 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 6).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Two feature selection techniques and a multilayer perceptron (MLP) neural network (NN) have been used in this study for human chromosome classification. The first technique is the `knock-out' algorithm and the second is the Principal Component Analysis (PCA). The `knock-out' algorithm emphasized the significance of the centrometric index and of the chromosome length, as features in chromosome classification. The PCA technique demonstrated the importance of retaining most of the image information whenever small training sets are used. However, the use of large training sets enables considerable data compression. Both techniques yield the benefit of using only about 70% of the available features to get almost the ultimate classification performance.

AB - Two feature selection techniques and a multilayer perceptron (MLP) neural network (NN) have been used in this study for human chromosome classification. The first technique is the `knock-out' algorithm and the second is the Principal Component Analysis (PCA). The `knock-out' algorithm emphasized the significance of the centrometric index and of the chromosome length, as features in chromosome classification. The PCA technique demonstrated the importance of retaining most of the image information whenever small training sets are used. However, the use of large training sets enables considerable data compression. Both techniques yield the benefit of using only about 70% of the available features to get almost the ultimate classification performance.

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Lerner B, Levinstein M, Rosenberg B, Guterman H , Dinstein I, Romem Y. Feature selection and chromosome classification using a multilayer perceptron neural network. In IEEE International Conference on Neural Networks - Conference Proceedings. Institute of Electrical and Electronics Engineers. 1994. p. 3540-3545. (IEEE International Conference on Neural Networks - Conference Proceedings). doi: 10.1109/icnn.1994.374905

Feature selection and chromosome classification using a multilayer perceptron neural network

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