# MultiKOC: Multi-One-Class Classifier Based K-Means Clustering
## 4 Results  
We conducted experiments on three different datasets. The first dataset is syntactic which consists of two classes positive and negative samples as shown on Figure 1. Here, the data contains two classes; positive and negative of 800 samples each. The positive examples are divided into four clusters beforehand. The second and the third data set are from the UCI repository [36]. In these data sets there are three classes. The Iris data set contains 3 classes of 50 samples each, where each class refers to a type of iris plant. The third data set is called \"Thyroid gland data\" which contains 150 samples from class \"normal\", 35 hyper, and 30 hypo class (in our experiments we assign normal as class 1, hyper as class 2 and hypo as class 3). For both data sets \"Iris\" and \"Thyroid gland data\", each time in our experiments, one-class out of the three classes was considered as the negative class, while the other two classes were considered as positive class. The generated datasets are summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Positive Class** & **Negative Class** \\ \hline SynData & blue & red \\ \hline Iris-setosa-versicolor & setosa and versicolor & verginica \\ \hline Iris-setosa-verginica & setosa and verginica & versicolor \\ \hline Iris-versicolor-verginica & versicolor and verginica & setosa \\ \hline newthyroid class 1 & class 1 & class 2 and class 3 \\ \hline class1\_2\_newthyroid & class 1 and class 2 & class 3 \\ \hline class1\_3\_newthyroid & class 1 and class 3 & class 2 \\ \hline class2\_3\_newthyroid & class 2 and class 3 & class 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the data sets. Positive and negative classes. Figure 3: Illustration of the proposed method. Firstly the algorithm clusters the positive dataset into k clusters, then triasm a one-class classifier for each cluster. Given a new instance, if at least one-classifier labeled it as a positive then it will be assigned as positive, otherwise negative. In each experiment for the OC classifiers, the positive data were split into two subsets--one for training and the other for testing--while all the examples from the negative class were used for testing and not seen in training the OC. All algorithms were trained using 80% of the positive class and the remaining 20%, together with all the negative examples, were used for testing. Each experiment was repeated one hundred times and the averaged results were reported. For the two-class classifiers we considered both the positive and negative data. Similarly, the data were split into training and testing sets, where 80% was used for training and 20% for testing. We tested the performance of MultiKOC using four different classifiers: J48, SVM, Naive Bayes, and Random Forest versus that of the classical one-class versions of these classifiers. Additionally, we tested the MultiKOC with different values of k that define the number of clusters generated by K-means. We have considered k = 1, 2,..., 6. The first experiment was conducted using the J48 classifier, as can be seen in Table 2. The performance of the multiKOC(J48) outperforms the classic one-class classifier. The second experiment's results are summarized in Table 3. The experiment was conducted using the Naive Bayes classifier. The performance of the proposed method using the Naive bayes classifier (i.e., multiKOC(J48)) outperforms the classic one-class Naive Bayes classifier.