On the Stability of Feature Selection Algorithms

Journal of Machine Learning Research, vol xx, 2017
Sarah Nogueira, Konstantinos Sechidis, Gavin Brown

We have developed statistical tools to understand the concept of stability in feature selection algorithms. This page allows you to estimate stability for a sequence of feature selection runs, independently of the algorithm used to select.

You do not have to upload your original features/data to us.
You do not have to upload the names of the features, or any other confidential information.

The only requirement is a text file with M rows, where each row represents one run of a feature selection algorithm. Each row should be a binary string of length d, the total number of features, with a 1 indicating the feature was chosen, and 0 not chosen. Your file can contain tabs, spaces or commas if you wish, but they will be stripped out. You can use the example file if you want, and upload it.

Drop a file into either of the boxes below, and the response will be your estimated stability, along with confidence intervals.

Drop two files, and if the dimensions M and d are the same, a hypothesis test of equality will be performed.

drop a file here
drop a file here