One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods and their thirteen variants based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, where three proposed classifiers belong to reconstruction based and three belong to boundary based. We are presenting both types of learning viz., online and offline learning for OCC. Out of six methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. We present a comprehensive discussion on these methods and their comparison to each other. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with all the present features of the toolbox.Various methods have been proposed to resolve the one-class classification problem. According to Pimental et al. [1], these methods can be broadly divided into five categories: (i) Probabilistic or density based (ii) Distance based (iii) Information theoretic techniques (iv) Domain or boundary based (v) Reconstruction based. Tax [2] divided OCC methods in three parts viz., density based, boundary based and reconstruction based. We have expanded the toolbox of OCC provided by Tax [2], so we divided our proposed work based on the category provided by Tax [2] only. We will provide detailed discussion about this in the next section. This paper is mainly focused on the last two categories i.e. domain or boundary and reconstruction based. Our literature survey also primarily focuses on these two categories only.The remaining paper is organized as follows. Literature survey about OCC is discussed in Section 2. Section 3 presents motivation of our proposed work. Section 4 provides a brief description of ELM and OSELM. Section 5 discusses about proposed work and three threshold deciding criteria. Subsequent...

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Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. LMKAD achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.

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