Skip to Main Content Machine learning methods are known to be
inefficient when faced with many features that are unnecessary
for rule discovery. In coping with this issue, many methods have
been proposed for selecting important features. Among them is
feature selection that selects a subset of discriminative
features or attribute for model building due to its ability to
avoid overfitting issue, improve model performance, provide
faster and producing reliable model. This paper proposes a new
method based on rough set algorithms, which is a rule-based data
mining method to select the important features in bioinformatics
datasets. Amino acid compositions are used as conditional
features for the classification task. However, our results
indicate that all amino acid composition features are equally
important thus selecting the features are unnecessary. We do
confirm the need of having a balance classes in classifying the
protein function by demonstrating an increase of more than 15%
in accuracy. Electrical Enginee