One-class Support Vector Machine Algorithm Research Articles

Disruption detection for a cognitive digital supply chain twin using hybrid deep learning

Recent disruptive events, such as COVID-19 and Russia–Ukraine conflict, had a significant impact of global supply chains. Digital supply chain twins have been proposed in order to provide decision makers with an effective and efficient tool to mitigate disruption impact. This paper introduces a hybrid deep learning approach for disruption detection within a cognitive digital supply chain twin framework to enhance supply chain resilience. The proposed disruption detection module utilises a deep autoencoder neural network combined with a one-class support vector machine algorithm. In addition, long-short term memory neural network models are developed to identify the disrupted echelon and predict time-to-recovery from the disruption effect. The obtained information from the proposed approach will help decision-makers and supply chain practitioners make appropriate decisions aiming at minimizing negative impact of disruptive events based on real-time disruption detection data. The results demonstrate the trade-off between disruption detection model sensitivity, encountered delay in disruption detection, and false alarms. This approach has seldom been used in recent literature addressing this issue.

A Health Status Assessment Method of Fluid Loop System Based on Hierarchical Multi-Information Fusion

As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems.

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.

Using Sensor Fusion and Machine Learning to Distinguish Pedestrians in Artificial Intelligence-Enhanced Crosswalks

Pedestrian safety is a major concern in urban areas, and crosswalks are one of the most critical locations where accidents can occur. This research introduces an intelligent crosswalk, employing sensor fusion and machine learning techniques to distinguish the presence of pedestrians and drivers. Upon detecting a pedestrian, the system proactively activates a warning light signal. This approach aims to quickly alert nearby people and mitigate potential dangers, thereby strengthening pedestrian safety. The system integrates data from radio detection and ranging sensors and a magnetic field sensor, using a hierarchical classifier. The One-Class support vector machine algorithm is used to classify objects in the radio detection and ranging data, while fuzzy logic is used to filter out targets from the magnetic field sensor. Additionally, this work presents a novel method for the manufacture of the road signaling system, using mixtures of resins, aggregates, and reinforcing fibers that are cold-injected into an aluminum mold. The mechanical, optical, and electrical characteristics were subjected to standardized tests, validating its autonomous operation in real-world conditions. The results revealed the system’s effectiveness in detecting pedestrians with a 99.11% accuracy and a 0.0% false-positive rate, marking a substantial improvement over the previous fuzzy logic-based system with an 81.33% accuracy. Attitude testing revealed a significant 33.33% reduction in pedestrian erratic behavior and a substantial decrease in driver speed (32.83% during the day and 70.6% during the night) compared to conventional crossings. Consequently, this comprehensive work offers a unique solution to pedestrian safety at crosswalks by showcasing the potential of machine learning techniques, particularly the One-Class support vector machine algorithm, in advancing road safety through precise and reliable pattern recognition.

High-dimensional time series analysis and anomaly detection: A case study of vehicle behavior modeling and unhealthy state detection

With the growing complexity of modern vehicles in the digital era, ensuring the reliability of vehicle system becomes a challenge that needs a responsive framework to monitor and alert the unhealthy states. An unhealthy state refers to a timestamp in which vehicle operates off the expected range of performance. Being able to detect an unhealthy state early not only helps to identify imminent performance failures but also indicates the necessity of deploying preventive maintenance. It is almost impossible to be aware of an imminent failure without modeling the behavior of various subsystems of vehicle. This study proposes a behavior modeling and anomaly detection approach to accurately analyze the operation of various vehicle subsystems and trigger early alerts of unhealthy states. We propose a Multi-Layer Long Short Term Memory (LSTM) network that is integrated with an Autoencoder architecture (ML-LSTMAE) to monitor and predict the operation of different subsystems of vehicle. Modern vehicles involve various subsystems with respective sensors that record various operating signals. The proposed model leverages multivariate time series data sequences that are recorded by different sensors to train an encoding–decoding scheme. We use One-Class Support Vector Machine (OCSVM) to analyze the reconstruction errors and build a support boundary. Then, the trained support boundary evaluates the prediction errors in test data to distinguish the healthy and unhealthy states of vehicle. We validate the efficiency of the proposed behavior modeling and anomaly detection approaches via evaluating two case study problems. First, a case study of an operating vehicle data that consists of 15 months time series records of 101 data channels. Second, a NASA bearing case study data that includes 20,110,378 observational records of four bearings time series sequences. The results of both case studies confirm the high accuracy of the proposed ML-LSTMAE network for learning the latent behavior of different operating subsystems. Moreover, the proposed OCSVM algorithm classifies the healthy and unhealthy states in both case studies, precisely.

Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42 percentage points in the area with dense low vegetation and by up to 14 percentage points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.

Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring

Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.

Versatile unsupervised anomaly detection method for RTE-based networks

Reliability and dependability are critical demands of the fourth industrial revolution that Real-time Ethernet (RTE) networks have to meet. The use of anomaly detection and prevention techniques can further enhance existing RTE networks. This work presents a general and efficient anomaly detection strategy based on machine learning techniques. The proposal is of general purpose since only normal (i.e not anomalous) traffic data and statistical features are used during the training phase of the classifier. These features are derived from data sets extracted from unsupervised traffic data using a sliding window algorithm. The efficiency of the proposal depends on the proper selection of the sliding window algorithm’ parameters. In this work, an optimization strategy appropriately selects the algorithm’s step and window size. An Adapted Correlation based on Feature Selection indicates the most relevant features reducing the data dimensionality, improving classifier performance and efficiency. Finally, a One-Class Support Vector Machine algorithm is trained and used for classifying the traffic data sets. The authors applied the proposed anomaly detection strategy in real network data (PROFINET and Ethernet/IP Networks). The results demonstrate the proposal’s efficiency and accuracy.

ML-SDNIDS: an attack detection mechanism for SDN based on machine learning

With the rapid development of network technology, there are more and more application scenarios of software defined networking (SDN), such as big data, cloud computing, internet of things, etc. However, the facilities in the SDN network face security issues such as DDoS attacks, network monitoring, and privacy. In addition, the SDN controller is also the main target of the attacker. This paper makes a simple analysis of the security risks in SDN and proposes a machine learning-based intrusion detection system for SDN (ML-SDNIDS). According to the characteristics of SDN, ML-SDNIDS uses autoencoder and one-class support vector machine algorithm to train intrusion detection model in the control plane, and uses P4 programming language combined with machine learning algorithm to realise real-time intrusion detection function in the data plane. And compared with the traditional SVM and OCSVM intrusion detection models in the latest intrusion detection dataset CIC-DDoS2019, the experimental results show that the scheme proposed in this paper has greatly improved the detection accuracy and the execution efficiency of the model. In addition, this experimental scheme can make the intrusion detection accuracy of data plane P4 switch as high as 97%, and its packet transmission efficiency is still millisecond.

Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients

In the field of financial monitoring, it is necessary to promptly obtain objective assessments of economic entities (in particular, credit institutions) for effective decision-making. Automation of the process of identifying unscrupulous credit institutions based on machine learning methods will allow regulatory authorities to quickly identify and suppress illegal activities. The aim of the research is to substantiate the possibilities of using machine learning methods and algorithms for the automatic identification of unscrupulous credit institutions. It is required to select a mathematical toolkit for analyzing data on credit institutions, which allows tracking the involvement of a bank in money laundering processes. The paper provides a comparative analysis of the results of processing data on the activities of credit institutions using classification methods — logistic regression, decision trees. The author applies support vector machine and neural network methods, Bayesian networks (Two-Class Bayes Point Machine), and anomaly search — an algorithm of a One-Class Support Vector Machine and a PCA-Based Anomaly Detection algorithm. The study presents the results of solving the problem of classifying credit institutions in terms of possible involvement in money laundering processes, the results of analyzing data on the activities of credit institutions by methods of detecting anomalies. A comparative analysis of the results obtained using various modern algorithms for the classification and search for anomalies is carried out. The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. Of the considered classification algorithms, the most accurate results were shown by the Two-Class Boosted Decision Tree (AdaBoost) algorithm. The research results can be used by the Bank of Russia and Rosfinmonitoring to automate the identification of unscrupulous credit institutions

Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management

Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA.

Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado

ABSTRACT Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities.

Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains

This article proposes a novel online fault diagnosis scheme for industrial powertrains without using historical faulty or labeled training data. The proposed method combines a one-class support vector machine (SVM) based anomaly detection and supervised convolutional neural network (CNN) algorithms to online detect multiple faults and fault severities under variable speeds and loads. The one-class SVM algorithm is to derive a score for defining faults or health classes in the first stage, and the resulting health classes are used as the training data for the CNN-based classifier in the second stage. Within this framework, the self-supervised learning of the proposed CNN algorithm allows the online diagnosis scheme to learn features based on the latest data. The effectiveness of the scheme is validated via a comparison study using experimental data from an in-house test setup. Finally, the online implementation of the proposed scheme on the test setup is briefly introduced.

OUTLIER DETECTION TECHNIQUE USING CT-OCSVM AND FUZZY RULE-BASED SYSTEM IN WIRELESS SENSOR NETWORKS

The development of Wireless Sensor Networks (WSNs) has been attained in the past few years due to its important using in wide range of application. The readings of data derived from WSN nodes are not always accurate and may contain abnormal data. This paper proposed an anomaly detection and classification algorithm in WSNs. At first, an integration of Contourlet Transform (CT) algorithm and One Class Support Vector Machine (OCSVM) algorithm (CT-OCSVM) is utilized to detect outliers then Fuzzy Inference System (FIS) is used to identify the source of these outliers. The underlying aim of this paper focuses on treating the collected streams of data as raw datum of an image, which is then passed through some filters using CT to get compressed size of directional subbands coefficients. The coefficients of CT are examined by OCSVM algorithm to detect anomalies. Finally the source of anomalies is identified based on using FIS and by exploiting the spatial temporal correlation existing between the sensed data. The integrated algorithm is tested using different types of filters. Real datasets collected from a small WSN constructed in a local lab are used for testing the integrated algorithms. The simulation results have shown a high rate of accurate classification with high detection rate and low false alarm rate.

Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection

Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can perform a series of Distributed Denial of Service (DDoS) attacks. Developing new methods to detect compromised IoT devices is urgent in order to mitigate the negative consequences of these IoT botnets since the existing IoT botnet detection methods still present some issues, such as, relying on labelled data, not being validated with newer botnets, and using very complex machine learning algorithms. Anomaly detection methods are promising for detecting IoT botnet attacks since the amount of available normal data is very large. One of the powerful algorithms that can be used for anomaly detection is One Class Support vector machine (OCSVM). The efficiency of the OCSVM algorithm depends on several factors that greatly affect the classification results such as the subset of features that are used for training OCSVM model, the kernel type, and its hyperparameters. In this paper, a new unsupervised evolutionary IoT botnet detection method is proposed. The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called Grey Wolf Optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnet problem. To prove the efficiency of the proposed method, its performance is evaluated using typical anomaly detection evaluation measures over a new version of a real benchmark dataset. The experimental results show that the proposed method outperforms all other algorithms in terms of true positive rate, false positive rate, and G-mean for all IoT device types. Also, it achieves the lowest detection time, while significantly reducing the number of selected features.

Adaptive Online One-Class Support Vector Machines with Applications in Structural Health Monitoring

One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.

Unsupervised obstacle detection in driving environments using deep-learning-based stereovision

Abstract A vision-based obstacle detection system is a key enabler for the development of autonomous robots and vehicles and intelligent transportation systems. This paper addresses the problem of urban scene monitoring and tracking of obstacles based on unsupervised, deep-learning approaches. Here, we design an innovative hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE). This hybrid auto-encode (HAE) model combines the greedy learning features of DBM with the dimensionality reduction capacity of AE to accurately and reliably detect the presence of obstacles. We combine the proposed hybrid model with the one-class support vector machines (OCSVM) to visually monitor an urban scene. We also propose an efficient approach to estimating obstacles location and track their positions via scene densities. Specifically, we address obstacle detection as an anomaly detection problem. If an obstacle is detected by the OCSVM algorithm, then localization and tracking algorithm is executed. We validated the effectiveness of our approach by using experimental data from two publicly available dataset, the Malaga stereovision urban dataset (MSVUD) and the Daimler urban segmentation dataset (DUSD). Results show the capacity of the proposed approach to reliably detect obstacles.

Automated novelty detection in the WISE survey with one-class support vector machines

Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources whose existence and properties cannot be easily predicted from earlier observations: novelties or even anomalies. Such objects can be efficiently sought for with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. OCSVM detects as anomalous those sources whose patterns - WISE photometric measurements in this case - are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but most importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

In this paper, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine's operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be nonstationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the nonstationary parts. The problem of high-dimensionality of the features is addressed by compressing them using the kernel principal component analysis so that more meaningful, lower-dimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine’s “normal” condition correspond to fuels and air-to-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the one-class support vector machine, the kernel width γ and optimization penalty parameter ν. This selection was made by searching along a fixed grid space of values and choosing the combination that provided the highest cross-validation accuracy. Nevertheless, there exist challenges that are discussed along with suggestions for future work that can be used to enhance similar novelty detection schemes.

One-class support higher order tensor machine classifier

One-class classification problems have been widely encountered in the fields that the negative class patterns are difficult to be collected, and the one-class support vector machine is one of the popular algorithms for solving them. However, one-class support vector machine is a vector-based learning algorithm, and it cannot work directly when the input pattern is a tensor. This paper proposes a tensor-based maximum margin classifier for one-class classification problems, and develops a One-Class Support Higher Order Tensor Machine (HO-OCSTM) which can separate most of the target patterns from the origin with the maximum margin in the higher order tensor space. HO-OCSTM directly employs the higher order tensors as the input patterns, and it is more proper for small sample study. Moreover, the direct use of tensor representation has the advantage of retaining the structural information of data, which helps improve the generalization ability of the proposed algorithm. We implement HO-OCSTM by the alternating projection method and solve a convex quadratic programming similar to the standard one-class support vector machine algorithm at each iteration. The experimental results have shown the high recognition accuracy of the proposed method.