  Machine learning-based techniques open up many opportunities and improvements
to derive deeper and more practical insights from data that can help businesses
make informed decisions. However, the majority of these techniques focus on the
conventional closed-set scenario, in which the label spaces for the training
and test sets are identical. Open set recognition (OSR) aims to bring
classification tasks in a situation that is more like reality, which focuses on
classifying the known classes as well as handling unknown classes effectively.
In such an open-set problem the gathered samples in the training set cannot
encompass all the classes and the system needs to identify unknown samples at
test time. On the other hand, building an accurate and comprehensive model in a
real dynamic environment presents a number of obstacles, because it is
prohibitively expensive to train for every possible example of unknown items,
and the model may fail when tested in testbeds. This study provides an
algorithm exploring a new representation of feature space to improve
classification in OSR tasks. The efficacy and efficiency of business processes
and decision-making can be improved by integrating OSR, which offers more
precise and insightful predictions of outcomes. We demonstrate the performance
of the proposed method on three established datasets. The results indicate that
the proposed model outperforms the baseline methods in accuracy and F1-score.
