Stable and Accurate Feature Selection
published: Oct. 20, 2009, recorded: September 2009, views: 4555
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In addition to accuracy, stability is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure, and perform both accuracy and stability measurements of MRMR (Minimum Redundancy Maximum Relevance) feature selection algorithm on different data sets. The two feature evaluation criteria used by MRMR, MID (Mutual Information Difference) and $MIQ$ (Mutual Information Quotient), result in similar accuracies, but MID is more stable. We also introduce a new feature selection criterion, MIDalpha, where redundancy and relevance of selected features are controlled by parameter alpha.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !