A Comparative Study on the Performance of Fuzzy Rule Base and Artificial Neural Network towards Classification of Yeast Data
| Autor: | Shrayasi Datta, J. Paulchoudhury |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Fuzzy rule
Basis (linear algebra) Artificial neural network Computer science business.industry Structural Classification of Proteins database Machine learning computer.software_genre Base (topology) Yeast ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer Cellular localization |
| Zdroj: | International Journal of Information Technology and Computer Science. 7:40-47 |
| ISSN: | 2074-9015 2074-9007 |
| DOI: | 10.5815/ijitcs.2015.05.06 |
| Popis: | Classification of yeast data plays an important role in the formation of medicines and in various chemical components. If the type of yeast can be recognized at the primary stage based on the initial characteristics of it, a lot of technical procedure can be avoided in the preparation of chemical and medical products. In this paper, the performance two classifying methodologies namely artificial neural network and fuzzy rule base has been compared, for the classification of proteins. The objective of this work is to classify the protein using the selected classifying methodology into their respective cellular localization sites based on their amino acid sequences. The yeast dataset has been chosen from UCI machine learning repository which has been used for this purpose. The results have shown that the classification using artificial neural network gives better prediction than that of fuzzy rule base on the basis of average error. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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