# MultiKOC: Multi-One-Class Classifier Based K-Means Clustering
## 5 Discussion  
This study suggests MultiKOC, a novel approach for performing one-class classification that is based on partitioning the training data into clusters to model each cluster by the one-class model.  
The current results show that it is possible to build up a multi-one-class classifier with a combined clustering beforehand process based only on positive examples yielding a significant improvement over the one-class and similar results as the two-class. However, the MultiKOC would include more interpretable classifiers than the two-class version as one can perform a deep analysis to explore the hidden structure of the data. Additionally, MultiKOC is robust at dealing with outliering examples, while the one-class version might add more clusters to capture those outliers and reduce their influence on the performance of the classifications.  
Further research could proceed in several interesting directions. First, the suitability of the framework of our approach for different data types could be investigated. Second, it would be interesting to apply our approach to other types of classifiers and to more robust clustering methods such as Mean-Shift [37].  
In the current version of MultiKOC we have considered only a one-class algorithm. One future approach is to perform an ensemble of OC and suggest a suitable voting procedure to assign the label to the new unlabeled instance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **2** & **3** & **4** & **5** \\ \hline MultiKOC(J48) & 0.233 & 0.263 & 0.167 & 0.167 \\ \hline MultiKOC(SVM) & 0.148 & 0.150 & 0.153 & 0.158 \\ \hline MultiKOC(NB) & 0.186 & 0.550 & 0.514 & 0.337 \\ \hline MultiKOC(RF) & 0.167 & 0.167 & 0.167 & 0.167 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results summary for MultiKOC method with 2, 3, 4, 5 clusters on Synthetic data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **2** & **3** & **4** & **5** \\ \hline MultiKOC(J48) & 0.344 & 0.325 & 0.290 & 0.272 \\ \hline MultiKOC(SVM) & 0.770 & 0.792 & 0.679 & 0.695 \\ \hline MultiKOC(NB) & 0.388 & 0.529 & 0.547 & 0.379 \\ \hline MultiKOC(RF) & 0.333 & 0.333 & 0.419 & 0.371 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results summary for MultiKOC method with 2, 3, 4, 5 clusters on Iris-setosa-versicolor data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **2** & **3** & **4** & **5** \\ \hline MultiKOC(J48) & 0.356 & 0.330 & 0.316 & 0.316 \\ \hline MultiKOC(SVM) & 0.369 & 0.355 & 0.347 & 0.342 \\ \hline MultiKOC(NB) & 0.505 & 0.771 & 0.437 & 0.490 \\ \hline MultiKOC(RF) & 0.385 & 0.443 & 0.373 & 0.347 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results summary for MultiKOC method with 2, 3, 4, 5 clusters on new thyroid class 1 data.  
**Author Contributions:** L.A., M.B., W.K. and M.Y. have contributed equally to this study in all its parts: conceptualization; methodology; software; validation; formal analysis; investigation; resources; data curation; writing--original draft preparation; writing--review and editing; visualization; supervision; project administration; funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** We used the Iris dataset from the UCI repository and the synthetic dataset that was constructed as described in the Experiments section.  
**Conflicts of Interest:** The authors declare no conflict of interest.