  In recent era prediction of enzyme class from an unknown protein is one of
the challenging tasks in bioinformatics. Day to day the number of proteins is
increases as result the prediction of enzyme class gives a new opportunity to
bioinformatics scholars. The prime objective of this article is to implement
the machine learning classification technique for feature selection and
predictions also find out an appropriate classification technique for function
prediction. In this article the seven different classification technique like
CRT, QUEST, CHAID, C5.0, ANN (Artificial Neural Network), SVM and Bayesian has
been implemented on 4368 protein data that has been extracted from UniprotKB
databank and categories into six different class. The proteins data is high
dimensional sequence data and contain a maximum of 48 features.To manipulate
the high dimensional sequential protein data with different classification
technique, the SPSS has been used as an experimental tool. Different
classification techniques give different results for every model and shows that
the data are imbalanced for class C4, C5 and C6. The imbalanced data affect the
performance of model. In these three classes the precision and recall value is
very less or negligible. The experimental results highlight that the C5.0
classification technique accuracy is more suited for protein feature
classification and predictions. The C5.0 classification technique gives 95.56%
accuracy and also gives high precision and recall value. Finally, we conclude
that the features that is selected can be used for function prediction.
