scholarly journals A Normalization Methods for Backpropagation: A Comparative Study

2017 ◽  
Vol 5 (4) ◽  
pp. 319 ◽  
Author(s):  
Adel S. Eesa ◽  
Wahab Kh. Arabo
Keyword(s):  
Comparative Study ◽  
Network Models ◽  
Z Score ◽  
Neural Network Models ◽  
Normalization Methods ◽  
Score Normalization ◽  
Learning Parameter ◽  
Artificial Neural Network Models ◽  
The Comparative Study ◽  
Better Than

Neural Networks (NN) have been used by many researchers to solve problems in several domains including classification and pattern recognition, and Backpropagation (BP) which is one of the most well-known artificial neural network models. Constructing effective NN applications relies on some characteristics such as the network topology, learning parameter, and normalization approaches for the input and the output vectors. The Input and the output vectors for BP need to be normalized properly in order to achieve the best performance of the network. This paper applies several normalization methods on several UCI datasets and comparing between them to find the best normalization method that works better with BP. Norm, Decimal scaling, Mean-Man, Median-Mad, Min-Max, and Z-score normalization are considered in this study. The comparative study shows that the performance of Mean-Mad and Median-Mad is better than the all remaining methods. On the other hand, the worst result is produced with Norm method.

Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi

2021 ◽  
Vol 5 (1) ◽  
pp. 114-122
Author(s):  
Gde Agung Brahmana Suryanegara ◽  
Adiwijaya ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Random Forest ◽  
The Body ◽  
Classification Method ◽  
Data Normalization ◽  
Z Score ◽  
Normal Limits ◽  
Normalization Methods ◽  
Score Normalization ◽  
Level Of Analysis ◽  
Better Than

Diabetes is a disease caused by high blood sugar in the body or beyond normal limits. Diabetics in Indonesia have experienced a significant increase, Basic Health Research states that diabetics in Indonesia were 6.9% to 8.5% increased from 2013 to 2018 with an estimated number of sufferers more than 16 million people. Therefore, it is necessary to have a technology that can detect diabetes with good performance, accurate level of analysis, so that diabetes can be treated early to reduce the number of sufferers, disabilities, and deaths. The different scale values for each attribute in Gula Karya Medika’s data can complicate the classification process, for this reason the researcher uses two data normalization methods, namely min-max normalization, z-score normalization, and a method without data normalization with Random Forest (RF) as a classification method. Random Forest (RF) as a classification method has been tested in several previous studies. Moreover, this method is able to produce good performance with high accuracy. Based on the research results, the best accuracy is model 1 (Min-max normalization-RF) of 95.45%, followed by model 2 (Z-score normalization-RF) of 95%, and model 3 (without data normalization-RF) of 92%. From these results, it can be concluded that model 1 (Min-max normalization-RF) is better than the other two data normalization models and is able to increase the performance of classification Random Forest by 95.45%.  

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