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: Contribution to conferencePaperpeer-review

46 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 languageEnglish
Pages3540-3545
Number of pages6
DOIs
StatePublished - 1 Jan 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period27/06/9429/06/94

ASJC Scopus subject areas

  • Software

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