# MultiKOC: Multi-One-Class Classifier Based K-Means Clustering
## 1 Introduction  
The aim of the one-class classification model is to distinguish data belonging to the target class from other possible classes [1; 2; 3; 4; 5]. This is an interesting problem because there are many real-world situations where a representative set of labeled examples for the second class is difficult to obtain or not available at all. This situation occurs in many real-word problems, such as in medical diagnosis for breast cancer detection [6; 7], in the prediction of protein-protein interactions [8], the one-class recognition of cognitive brain functions [3], text mining [9], functional Magnetic Resonance Imaging [10], signature verification [11], biometrics [12] and bioinformatics [5; 13; 14; 15; 16], and social media [17]. In the literature, a vast amount of research has been carried out to tackle the problem of how to implement a multi-class classifier by an ensemble of one-class classifiers [18; 19]. [PERSON] et al. [20] proposed a method for combining different one-class classifiers for the problem of image retrieval. They reported that combining multi-SVM-based classifiers improves the retrieval precision. In a similar way, [PERSON] et al. [21] suggest combining different one-class classifiers to improve the performance and the robustness of the classification for the handwritten digit recognition problem. A multi-one-class SVMs technique (OC-SVM) that combines a beforehand clustering process for detecting hidden messages in digital images was provided by [PERSON] et al. [22]. They showed that a multi-one-class SVM significantly simplifies the training stage of the classifiers and that even though the overall detection improves with an increasing number of hyperspheres, the false-positive rate increases considerably when the numberof the hyperspheres increases. [PERSON] et al. [23] suggested a different multiple one-class classification approach called TUPSO, which combines multi-one-class classifiers via a metaclassifier. They showed that TUPSO outperforms existing methods such as the OC-SVM. [PERSON] et al. [24] proposed multiple one-class classifiers to deal with the nonlinear classification and the feature space problem. The multiple one-class classifiers were trained on each class in order to extract a decision function based on minimum distance rules. This proposed method outperforms the OC-SVM, as shown in their experiments. In the domain of the computational biology community much work exists on multiple one-class classification. A multi-one-class classification approach to detect novelty in gene expression data was proposed by [PERSON] et al. [25]. The approach combined different one-class classifiers such as the OC-KNN and OC-Kmeans. For a given sample, the final classification is considered by the majority votes of all classifiers. It was shown that the robustness of the classification was increased because each classifier judges the sample from a different point-of-view. For the avian influenza outbreak classification problem, a similar approach was provided by [PERSON] et al. [26]. In classification we assume that two-class data consist of two pure compact clusters of data, but in many cases one of the clusters might consist of multiple subclusters. For a certain dataset, a special method is required and the one-class reveals insufficient results. In this paper we propose a new approach called MultiKOC (Multi-one-class classifier based on K-means) which is an ensemble of one-class classifiers that, as a first step, devises the positive class into clusters or-subdata using the K-means applied to the examples of the data (not on the features space) and in the second step it trains a one-class classifier for each cluster (subdata). The main idea of our approach is to execute the K-means clustering algorithm over the positive examples. Next, a one-class classifier for each cluster is constructed separately. For a given new sample, our algorithm applies all the generated one-class classifiers.