## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Name** & **Features** & **Examples** & **Anomalies** &
\begin{tabular}{c} **Anomalies** \\ **Percentage [\%]** \\ \end{tabular} \\ \hline
1 & annthyroid & 6 & 7200 & 534 & 7.42 \\ \hline
2 & arrhythmia & 274 & 452 & 66 & 14.60 \\ \hline
3 & breastw & 9 & 683 & 239 & 34.99 \\ \hline
4 & cardio & 21 & 1831 & 176 & 9.61 \\ \hline
5 & glass & 9 & 214 & 9 & 4.21 \\ \hline
6 & ionosphere & 33 & 351 & 126 & 35.90 \\ \hline
7 & letter & 32 & 1600 & 100 & 6.25 \\ \hline
8 & lympho & 18 & 148 & 6 & 4.05 \\ \hline
9 & mammography & 6 & 11,183 & 260 & 2.32 \\ \hline
10 & pendigits & 16 & 6870 & 156 & 2.27 \\ \hline
11 & pima & 8 & 768 & 268 & 34.90 \\ \hline
12 & satellite & 36 & 6435 & 2036 & 31.64 \\ \hline
13 & thyroid & 6 & 3772 & 93 & 2.47 \\ \hline
14 & vertebral & 6 & 240 & 30 & 12.50 \\ \hline
15 & vowels & 12 & 1456 & 50 & 3.43 \\ \hline
16 & wbc & 30 & 378 & 21 & 5.56 \\ \hline
17 & wine & 13 & 129 & 10 & 7.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summery of the data used in the numerical experiments. * iforest--Isolation Forest  
* knn--k-Nearest Neighbors Detector  
* lof--Local Outlier Factor  
* mcd--Minimum Covariance Determinant  
* pca--Principal Component Analysis  
* sod--Subspace Outlier Detection  
* sos--Stochastic Outlier Selection  
In the computations, svm--One-Class SVM detector--was not included due to excessively long computation times. However, it was observed that for those data where computations could be performed, it did not yield competitive results. For more details regarding the algorithms, the reader can be referred to the PyCaret library documentation. To compare the obtained results, several typical measures of the quality of the anomaly detection model were used: accuracy, that is, the percentage of correct classifications; AUC, that is, Area Under the Curve; precision; recall; F1 score; Cohen's kappa; and Matthews correlation coefficient. For all these statistics, a higher value indicates a better model. From the obtained results, it follows that there is no clear winner. However, the proposed unsupervised negative selection algorithm presents itself as a worthy competitor to other classical solutions. In the case of certain norms and data, it achieves the best results. In Table 2, the results for accuracy are presented. The proposed algorithm achieved the best results for six problems, and it was the best result among all of the algorithms. Of course, accuracy is not the best measure in the case of anomaly detection problems, where one of the classes is very underrepresented. Table 3 presents the results of the AUC metric. Here too, the proposed algorithm presents itself as a valuable tool, winning in seven cases. Further analysis of the results from Table 4, presenting the results for precision, and Table 5, presenting recall, indicates that the proposed algorithm achieves good results primarily due to high values of recall, which indicates its strong capabilities in actually detecting existing anomalies, even if this is at the cost of slightly reduced precision and increased chance of a false alarm. This points to its potential applications in problems where maximizing the recall metric is important. Combining the precision and recall measures in the F1 score in Table 6 still indicates the competitiveness of the proposed method; it wins in four cases, which was achieved only by the _cluster_ algorithm. The values of Cohen's kappa in Table 7 and Matthews correlation coefficient in Table 8 also show that, depending on the problem, the proposed solution can be a good choice compared to classical algorithms.