The week's pick
Reseach Article
A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 4 |
| Year of Publication: 2017 |
| Authors: Kedar Potdar, Taher S. Pardawala, Chinmay D. Pai |
10.5120/ijca2017915495
|
Kedar Potdar, Taher S. Pardawala, Chinmay D. Pai . A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers. International Journal of Computer Applications. 175, 4 ( Oct 2017), 7-9. DOI=10.5120/ijca2017915495
Abstract
In classification analysis, the dependent variable is frequently influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables. Machine Learning algorithms accept only numerical inputs, hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. This paper presents a comparative study of seven categorical variable encoding techniques to be used for classification using Artificial Neural Networks on a categorical dataset. The Car Evaluation dataset provided by UCI is used for training. Results show that the data encoded with Sum Coding and Backward Difference Coding technique give highest accuracy as compared to the data pre-processed by rest of the techniques.
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