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.

研究通过开发数据抽象协议，将小样本和不完整数据的分类能力提高至最优，通过比较使用随机森林作为基准的可解释方法，发现 Small and Incomplete Dataset Analyser 在数据不完整的情况下能够始终保持高准确性。