group_admissibility

admissibility

Evaluate the value of the admissibility function `kappa`.

The framework proposed in Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>, together with an interactive Shiny dashboard. 'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection.

Anna Jenul

UBayFS

A User-Guided Bayesian Framework for Ensemble Feature Selection

Stefan Schrunner

Kristian Hovde Liland

Oliver Tomic

Jürgen Pilz

group_admissibility function

<dl><dt>state</dt>
<dd>a binary membership vector describing a feature set</dd>
<dt>constraints</dt>
<dd>group of constraints with common block matrix</dd>
<dt>log</dt>
<dd>whether the admissibility should be returned on log scale</dd>
<dt>constraint_list</dt>
<dd>a list of constraint groups, each containing a matrix `A` and a vector `b` representing the inequality system `Ax&lt;=b`, a vector `rho`, and a matrix `block_matrix`</dd></dl>

Arguments

<ul>
<li><code>group_admissibility()</code>: computes admissibility for a group of constraints (with a common block).</li>
</ul>

Functions

Admissibility for constraint group — group_admissibility

<dl>

<dt>state</dt>
<dd>a binary membership vector describing a feature set</dd>


<dt>constraints</dt>
<dd>group of constraints with common block matrix</dd>


<dt>log</dt>
<dd>whether the admissibility should be returned on log scale</dd>


<dt>constraint_list</dt>
<dd>a list of constraint groups, each containing a matrix `A` and a vector `b` representing the inequality system `Ax&lt;=b`, a vector `rho`, and a matrix `block_matrix`</dd>

</dl>

group_admissibility: Admissibility for constraint group

Description

Usage

Value

Arguments

Functions