Abstract: Public health is one of the most critical issues today;
therefore, there is great interest to improve technologies in the area
of diseases detection. With machine learning and feature selection,
it has been possible to aid the diagnosis of several diseases such
as cancer. In this work, we present an extension to the Heat Map
Based Feature Selection algorithm, this modification allows automatic
threshold parameter selection that helps to improve the generalization
performance of high dimensional data such as mass spectrometry.
We have performed a comparison analysis using multiple cancer
datasets and compare against the well known Recursive Feature
Elimination algorithm and our original proposal, the results show
improved classification performance that is very competitive against
current techniques.