Related Experiment Video
Updated: Apr 12, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
On the feature selection criterion based on an approximation of multidimensional mutual information
Kiran S Balagani1, Vir V Phoha
1Center for Secure Cyberspace, Computer Science,Louisiana Tech University, Nethken Hall, 600 W. Arizona Ave., Ruston,LA 71272, USA. ksb011@latech.edu
Abstract:
We derive the feature selection criterion presented in [CHECK END OF SENTENCE] and [CHECK END OF SENTENCE] from the multidimensional mutual information between features and the class. Our derivation: 1) specifies and validates the lower-order dependency assumptions of the criterion and 2) mathematically justifies the utility of the criterion by relating it to Bayes classification error.
Related Concept Videos
Frequency-dependent Selection
Expected Frequencies in Goodness-of-Fit Tests
Multiple Comparison Tests
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
Routh-Hurwitz Criterion I
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
Routh-Hurwitz Criterion II
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...

