Review of current Online Dynamic Unsupervised Feed Forward Neural Network classification

HAITHAM SABAH, HASAN and ROYA, ASADI and SAMEEM ABDUL, KAREEM (2014) Review of current Online Dynamic Unsupervised Feed Forward Neural Network classification. In: International Conference on Advances in Computer Science and Electronics Engineering - CSEE 2014, 08-09 March, 2014, Kuala Lumpur, Malaysia.

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Abstract

Online Dynamic Unsupervised Feed Forward Neural Network (ODUFFNN) classification is suitable to be applied in different research areas and environments such as email logs, networks, credit card transactions, astronomy and satellite communications. Currently, there are a few strong methods as ODUFFNN classification, although they have general problems. The goal of this research is an investigation of the critical problems and comparison of current ODUFFNN classification. For experimental results, Evolving Self-Organizing Map (ESOM) and Dynamic Self-Organizing Map (DSOM) as strong related methods are considered; and also they applied some difficult datasets for clustering from the UCI Dataset Repository. The results of the ESOM and the DSOM methods are compared with the results of some related clustering methods. The clustering time is measured by the number of epochs and CPU time usage. The clustering accuracies of methods are measured by employing F-measure through an average of three times performances of clustering methods. The memoryusage and complexity are measured by the number of input values, training iterations, clusters; and densities of clusters.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural Network (NN) model, Feed Forward Unsupervised Classification, Training, Epoch, Online Dynamic Unsupervised Feed Forward Neural Network (ODUFFNN) (key word
Depositing User: Mr. John Steve
Date Deposited: 11 May 2019 12:25
Last Modified: 11 May 2019 12:25
URI: http://publications.theired.org/id/eprint/2232

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