Paper Title

Robust Neural Network Classifier

Authors

  • Ali Refaee Abd Ellah Mohamed
  • Mohamed Hassan Essai
  • Mohamed Mohamed el- Sayed Zahra

Keywords

Robust Statistics, Feed-Forward Neural Networks, M-Estimators, Classification, Robust classifier.

Abstract

Classification is a data mining technique used to predict Patterns’ membership. Pattern classification involves building a function that maps the input feature space to an output space of two or more than two classes. Neural Networks (NN) are an effective tool in the field of pattern classification. The success of NN is highly dependent on the performance of the training process and hence the training algorithm. Many training algorithms have been proposed so far to improve the performance of neural networks. Usually a traditional backpropagation learning algorithm (BPLA), which minimizes the mean squared error (MSE – cost function) of the training data, be used in the process of training neural networks. However (MSE) based learning algorithm is not robust in presence of outliers that may pollute the training data. In our work we aim to present another cost functions which backpropagation learning algorithm based on in order to improve the robustness of neural network training by employing a family of robust statistics estimators, commonly known as M-estimators, and hence obtain robust NN classifiers. Comparative study between robust classifiers and non-robust (traditional) classifiers was established in paper using crab classification problem.

Article Type

Published

How To Cite

Ali Refaee Abd Ellah Mohamed, Mohamed Hassan Essai, Mohamed Mohamed el- Sayed Zahra. "Robust Neural Network Classifier".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.1, Issue 3, pp.0, URL :https://rjwave.org/ijedr/papers/IJEDR1303065.pdf

Issue

Volume 1 Issue 3 

Pages. 0

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