The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active...
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| Format: | Thesis |
| Language: | English English |
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2012
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| Online Access: | http://eprints.uthm.edu.my/2156/ |
| Abstract | Abstract here |
| _version_ | 1855520492931252224 |
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| author | Atomi, Walid Hasen |
| author_facet | Atomi, Walid Hasen |
| author_sort | Atomi, Walid Hasen |
| description | The artificial neural network (ANN) has recently been applied in many areas, such as
medical, biology, financial, economy, engineering and so on. It is known as an excellent
classifier of nonlinear input and output numerical data. Improving training efficiency of
ANN based algorithm is an active area of research and numerous papers have been
reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained
with back-propagation artificial neural network (BP-ANN) method is highly influenced
by the size of the data-sets and the data-preprocessing techniques used. This work
analyzes the advantages of using pre-processing datasets using different techniques in
order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal
Scaling Normalization preprocessing techniques were evaluated. The simulation results
showed that the computational efficiency of ANN training process is highly enhanced
when coupled with different preprocessing techniques. |
| format | Thesis |
| id | uthm-2156 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English English |
| publishDate | 2012 |
| record_format | EPrints |
| record_pdf | Restricted |
| spelling | uthm-21562021-10-31T03:16:55Z http://eprints.uthm.edu.my/2156/ The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems Atomi, Walid Hasen QA Mathematics QA71-90 Instruments and machines The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques. 2012-12 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/2156/1/24p%20WALID%20HASEN%20ATOMI.pdf text en http://eprints.uthm.edu.my/2156/2/WALID%20HASEN%20ATOMI%20WATERMARK.pdf Atomi, Walid Hasen (2012) The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems. Masters thesis, Universiti Tun Hussein Malaysia. |
| spellingShingle | QA Mathematics QA71-90 Instruments and machines Atomi, Walid Hasen The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| thesis_level | Master |
| title | The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| title_full | The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| title_fullStr | The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| title_full_unstemmed | The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| title_short | The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| title_sort | effect of data preprocessing on the performance of artificial neural networks techniques for classification problems |
| topic | QA Mathematics QA71-90 Instruments and machines |
| url | http://eprints.uthm.edu.my/2156/ |
| work_keys_str_mv | AT atomiwalidhasen theeffectofdatapreprocessingontheperformanceofartificialneuralnetworkstechniquesforclassificationproblems AT atomiwalidhasen effectofdatapreprocessingontheperformanceofartificialneuralnetworkstechniquesforclassificationproblems |