Feature selection methods help machine learning algorithms produce faster and more accurate solutions because they reduce the input dimensionality and they can eliminate irrelevant or redundant features. Entropy based feature selection algorithms such as MRMR . Minimum Redundancy Maximum Relevance and FCBF , Fast Correlation Based Filter are preferred feature selection methods because they are very fast and produce sets of features that result in quite accurate classifiers. Besides accuracy stability is another measure of goodness for a feature selection algorithm. A feature selection algorithm is said to be stab le if changes in the identity of data points available for feature selection still result in the same or similar sets of features. In this study we first developed a net stability measurement and performed accuracy and stability measurements of MRMR when it is used on different data sets. We found out that the two feature selection methods within MRMR MID and MIQ result in features with similar accuracy off On the other hand, MD results in more stable feature sets than MIQ and therefore should be referred over MIQ specially for small number of available samples.