get_synthetic_cases_from_matrix

This function creates synthetic cases for balancing the training with an object of the class
TEClassifierRegular or TEClassifierProtoNet.

In social and educational settings, the use of Artificial
Intelligence (AI) is a challenging task. Relevant data is often only
available in handwritten forms, or the use of data is restricted by
privacy policies. This often leads to small data sets. Furthermore, in
the educational and social sciences, data is often unbalanced in terms
of frequencies. To support educators as well as educational and social
researchers in using the potentials of AI for their work, this package
provides a unified interface for neural nets in 'PyTorch' to deal with
natural language problems. In addition, the package ships with a shiny
app, providing a graphical user interface. This allows the usage of
AI for people without skills in writing python/R scripts. The tools
integrate existing mathematical and statistical methods for dealing
with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al.
(2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the
creation of synthetic cases (e.g. Bunkhumpornpat et al. (2012)
<doi:10.1007/s10489-011-0287-y>). Performance evaluation of AI is
connected to measures from content analysis which educational and
social researchers are generally more familiar with (e.g. Berding &
Pargmann (2022) <doi:10.30819/5581>, Gwet (2014)
<ISBN:978-0-9708062-8-4>, Krippendorff (2019)
<doi:10.4135/9781071878781>). Estimation of energy consumption and CO2
emissions during model training is done with the 'python' library
'codecarbon'. Finally, all objects created with this package allow to
share trained AI models with other people.

Berding Florian

aifeducation

Artificial Intelligence for Education

Tykhonova Yuliia

Pargmann Julia

Leube Anna

Riebenbauer Elisabeth

Rebmann Karin

Slopinski Andreas

get_synthetic_cases_from_matrix function

<dl><dt>matrix_form</dt>
<dd>Named <code>matrix</code> containing the text embeddings in a matrix form.</dd>
<dt>times</dt>
<dd><code>int</code> for the number of sequences/times.</dd>
<dt>features</dt>
<dd><code>int</code> for the number of features within each sequence.</dd>
<dt>target</dt>
<dd>Named <code>factor</code> containing the labels of the corresponding embeddings.</dd>
<dt>sequence_length</dt>
<dd><code>int</code> Length of the text embedding sequences.</dd>
<dt>method</dt>
<dd><code>vector</code> containing strings of the requested methods for generating new cases. Currently "smote",
"dbsmote", and "adas" from the package smotefamily are available.</dd>
<dt>min_k</dt>
<dd><code>int</code> The minimal number of nearest neighbors during sampling process.</dd>
<dt>max_k</dt>
<dd><code>int</code> The maximum number of nearest neighbors during sampling process.</dd></dl>

Arguments

Create synthetic cases for balancing training data — get_synthetic_cases_from_matrix

<dl>

<dt>matrix_form</dt>
<dd>Named <code>matrix</code> containing the text embeddings in a matrix form.</dd>


<dt>times</dt>
<dd><code>int</code> for the number of sequences/times.</dd>


<dt>features</dt>
<dd><code>int</code> for the number of features within each sequence.</dd>


<dt>target</dt>
<dd>Named <code>factor</code> containing the labels of the corresponding embeddings.</dd>


<dt>sequence_length</dt>
<dd><code>int</code> Length of the text embedding sequences.</dd>


<dt>method</dt>
<dd><code>vector</code> containing strings of the requested methods for generating new cases. Currently "smote",
"dbsmote", and "adas" from the package smotefamily are available.</dd>


<dt>min_k</dt>
<dd><code>int</code> The minimal number of nearest neighbors during sampling process.</dd>


<dt>max_k</dt>
<dd><code>int</code> The maximum number of nearest neighbors during sampling process.</dd>

</dl>

get_synthetic_cases_from_matrix: Create synthetic cases for balancing training data

Description

Usage

Value

Arguments

See Also