The Significance of Data Abstraction Methods in Machine Learning
  Classification Processes for Critical Decision-Making
Karol Capała Paulina Tworek Jose Sousa
The applicability of widely adopted machine learning (ML) methods to
classification is circumscribed by the imperatives of explicability and
uncertainty, particularly evident in domains such as healthcare, behavioural
sciences, and finances, wherein accountability assumes priority. Recently,
Small and Incomplete Dataset Analyser (SaNDA) has been proposed to enhance the
ability to perform classification in such domains, by developing a data
abstraction protocol using a ROC curve-based method. This paper focuses on
column-wise data transformations called abstractions, which are crucial for
SaNDA's classification process and explores alternative abstractions protocols,
such as constant binning and quantiles. The best-performing methods have been
compared against Random Forest as a baseline for explainable methods. The
results suggests that SaNDA can be a viable substitute for Random Forest when
data is incomplete, even with minimal missing values. It consistently maintains
high accuracy even when half of the dataset is missing, unlike Random Forest
which experiences a significant decline in accuracy under similar conditions.