## 4 Experimental Results
### Classification of the Lesions  
The proposed classification algorithm with six cancer-related datasets was tested, and the results are presented with the classification performances in the following section, including the comparison with two classic algorithms utilized in experiments and published in the literature.  
The datasets used were:  
1. Breast Cancer Digital Repository (BCDR) [46]. The Faculty of Medicine of the University of Porto, Portugal provided this dataset, and it is used to explore the computer-based detection and diagnosis methods.
2. Breast Cancer Wisconsin (Original) Data Set (BCWO) [47; 48]. The University of Wisconsin, USA provided this dataset. This dataset contains data collected from clinical cases by Dr. [PERSON].
3. Breast Cancer Wisconsin (Prognostic) Data Set (BCWP) [48]. Dr. [PERSON] provided this dataset, and it contains the follow-up of breast cancer cases.
4. Lung Cancer Data Set (LCDS) [48]. [PERSON] and [PERSON] used this dataset to apply the k-nearest neighbor method, showing a biased result. It contains data on lung cancer patients.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Mean Time (sec)** \\ \hline Otsu method & 0.051 \\ Simulated annealing & 130.406 \\ Novel bat algorithm & 53.830 \\ Genetic algorithm & 170.830 \\ \hline \end{tabular}
\end{table}
Table 3: Mean time for each algorithm.  
Figure 7: \(F\) and \(F^{\prime}\) Mean error values of each algorithm.  
* Mammographic Mass Data Set (MMDS) [49]. [PERSON] donated this dataset to the University of California, Irvine (UCI) repository in October 2007. It contains data of mammographic mass lesions from Breast Imaging Report Database Systems.
* Haberman's Survival Data Set (HSDS) [48]. [PERSON] donated this dataset in 1999. It contains data related to the survival of patients who underwent breast cancer surgery.  
Similarly, a brief description of the classical algorithms used for classification is included. Support Vector Machines (SVM) [50] are algorithms used for classification and regression. These algorithms build a model that represents the patterns of the training set. Their main objective is to find a hyperplane that separates two classes. These algorithms work ideally for two classes; however, it is possible to use strategies that allow them to be used to build a model able to separate two classes.  
Repeated incremental pruning to produce error reduction (RIPPER) [51] is an algorithm based on association rules with reduced error pruning; this technique is frequently used in decision tree algorithms. The generation of rules is performed by applying pruning operators to reduce the error. The algorithm ends when the error is increased after a pruning operation.  
Regarding the classification process, we present the results of classifier accuracy comparing the two algorithms with the artificial immune system in Table 4.  
We repeated the classification process 10 times and averaged the performance of each algorithm. The classification accuracy is calculated as the number of correctly classifying elements among the total number of elements. The best accuracy for each dataset is bolded in Table 4.  
As it can be observed, the artificial immune system (AIS) proposed got the best performance in four out of six datasets, which is interesting considering that the other algorithms are widely used in the literature.