  In this paper we propose a novel method for semi-supervised anomaly detection
(SSAD). Our classifier is named QMS22 as its inception was dated 2022 upon the
framework of quadratic multiform separation (QMS), a recently introduced
classification model. QMS22 tackles SSAD by solving a multi-class
classification problem involving both the training set and the test set of the
original problem. The classification problem intentionally includes classes
with overlapping samples. One of the classes contains mixture of normal samples
and outliers, and all other classes contain only normal samples. An outlier
score is then calculated for every sample in the test set using the outcome of
the classification problem. We also include performance evaluation of QMS22
against top performing classifiers using ninety-five benchmark imbalanced
datasets from the KEEL repository. These classifiers are BRM (Bagging-Random
Miner), OCKRA (One-Class K-means with Randomly-projected features Algorithm),
ISOF (Isolation Forest), and ocSVM (One-Class Support Vector Machine). It is
shown by using the area under the curve of the receiver operating
characteristic curve as the performance measure, QMS22 significantly
outperforms ISOF and ocSVM. Moreover, the Wilcoxon signed-rank tests reveal
that there is no statistically significant difference when testing QMS22
against BRM nor QMS22 against OCKRA.
