A survey of recent trends in one class classification <s> Application Domain Used <s> Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the first mass of a pair of said unbalanced masses being rigidly fastened to said shaft and with said second mass of said pair being movably arranged relative to said first mass, means for controlling and regulating the conveying rate during conveyer operation by varying the rotational speed of said motor between predetermined minimum and maximum values, said second mass being movably outwardly by centrifugal force against the pressure of spring means, said spring means being prestressed in such a manner that said second mass is, at rotational motor speeds lower than said minimum speed, held in its initial position, and at motor speeds between said lower and upper values in positions which are radially offset with respect to the axis of said motor to an extent depending on the value of said rotational motor speed. <s> BIB001 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> In one-class classification, one class of data, called the target class, has to be distinguished from the rest of the feature space. It is assumed that only examples of the target class are available. This classifier has to be constructed such that objects not originating from the target set, by definition outlier objects, are not classified as target objects. In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. It models a hypersphere around the target set, and by the introduction of kernel functions, more flexible descriptions are obtained. In the original optimization of the SVDD, two parameters have to be given beforehand by the user. To automatically optimize the values for these parameters, the error on both the target and outlier data has to be estimated. Because no outlier examples are available, we propose a method for generating artificial outliers, uniformly distributed in a hypersphere. An (relative) efficient estimate for the volume covered by the one-class classifiers is obtained, and so an estimate for the outlier error. Results are shown for artificial data and for real world data. <s> BIB002 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schoelkopf et al. and a somewhat different version of one-class SVM based on identifying "outlier" data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called "Hadamard" representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on "bottleneck" compression generated filters.The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust. <s> BIB003 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation. <s> BIB004 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Web page classification is one of the essential techniques for Web mining. Specifically, classifying Web pages of a user-interesting class is the first step of mining interesting information from the Web. However, constructing a classifier for an interesting class requires laborious pre-processing such as collecting positive and negative training examples. For instance, in order to construct a "homepage" classifier, one needs to collect a sample of homepages (positive examples) and a sample of non-homepages (negative examples). In particular, collecting negative training examples requires arduous work and special caution to avoid biasing them. We introduce in this paper the Positive Example Based Learning (PEBL) framework for Web page classification which eliminates the need for manually collecting negative training examples in pre-processing. We present an algorithm called Mapping-Convergence (M-C) that achieves classification accuracy (with positive and unlabeled data) as high as that of traditional SVM (with positive and negative data). Our experiments show that when the M-C algorithm uses the same amount of positive examples as that of traditional SVM, the M-C algorithm performs as well as traditional SVM. <s> BIB005 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques. <s> BIB006 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> With the tremendous growth of the Internet, information system security has become an issue of serious global concern due to the rapid connection and accessibility. Developing effective methods for intrusion detection, therefore, is an urgent task for assuring computer & information system security. Since most attacks and misuses can be recognized through the examination of system audit log files and pattern analysis therein, an approach for intrusion detection can be built on them. First we have made deep analysis on attacks and misuses patterns in log files; and then proposed an approach using support vector machines for anomaly detection. It is a one-class SVM based approach, trained with abstracted user audit logs data from 1999 DARPA. <s> BIB007 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Single-class classifier learning is the problem of learning a classifier from a set of training examples in which only examples of the target class are present. Most existing approaches to this problem are based on density estimation and hence suffer from the usual problems associated with estimating probability densities in high dimensional spaces. This paper describes how feedforward neural networks can be used to learn a classifier from a dataset consisting of (labeled) examples of the target class (positive examples) together with a corpus of unlabeled (positive and negative) examples. An application of the technique to the prediction of mineral deposit location is provided, and empirical results are presented. <s> BIB008 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> We extend prior research on masquerade detection using UNIX commands issued by users as the audit source. Previous studies using multi-class training requires gathering data from multiple users to train specific profiles of self and non-self for each user. Oneclass training uses data representative of only one user. We apply one-class Naive Bayes using both the multivariate Bernoulli model and the Multinomial model, and the one-class SVM algorithm. The result shows that oneclass training for this task works as well as multi-class training, with the great practical advantages of collecting much less data and more efficient training. One-class SVM using binary features performs best among the oneclass training algorithms. <s> BIB009 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Web page classification is one of the essential techniques for Web mining because classifying Web pages of an interesting class is often the first step of mining the Web. However, constructing a classifier for an interesting class requires laborious preprocessing such as collecting positive and negative training examples. For instance, in order to construct a "homepage" classifier, one needs to collect a sample of homepages (positive examples) and a sample of nonhomepages (negative examples). In particular, collecting negative training examples requires arduous work and caution to avoid bias. The paper presents a framework, called positive example based learning (PEBL), for Web page classification which eliminates the need for manually collecting negative training examples in preprocessing. The PEBL framework applies an algorithm, called mapping-convergence (M-C), to achieve high classification accuracy (with positive and unlabeled data) as high as that of a traditional SVM (with positive and negative data). M-C runs in two stages: the mapping stage and convergence stage. In the mapping stage, the algorithm uses a weak classifier that draws an initial approximation of "strong" negative data. Based on the initial approximation, the convergence stage iteratively runs an internal classifier (e.g., SVM) which maximizes margins to progressively improve the approximation of negative data. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. We present the M-C algorithm with supporting theoretical and experimental justifications. Our experiments show that, given the same set of positive examples; the M-C algorithm outperforms one-class SVMs, and it is almost as accurate as the traditional SVMs. <s> BIB010 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> In the authorship verification problem, we are given examples of the writing of a single author and are asked to determine if given long texts were or were not written by this author. We present a new learning-based method for adducing the "depth of difference" between two example sets and offer evidence that this method solves the authorship verification problem with very high accuracy. The underlying idea is to test the rate of degradation of the accuracy of learned models as the best features are iteratively dropped from the learning process. <s> BIB011 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> In this paper, several one-class classification methods are investigated in pixel space and PCA (Principal component Analysis) subspace having in mind the need of finding suitable learning and classification methods to support natural language grounding in the context of Human-Robot Interaction. Face and non-face classification is used as an example to demonstrate effectiveness of these one-class classifiers. The idea is to train target class models with only target (face) patterns, but still keeping good discrimination over outlier (never seen non-target) patterns. Some discussion is given and promising results are reported. <s> BIB012 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Novelty Detection techniques might be a promising way of dealing with high-dimensional classification problems in Bioinformatics. This paper presents the early results of the use of a One-Class SVM approach to detect novel classes in two Bioinformatics databases. The results are compatible with the theory and inspire further investigations. <s> BIB013 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Fast incipient machine fault diagnosis is becoming one of the key requirements for economical and optimal process operation management. Artificial neural networks have been used to detect machine faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for machine fault detection and classification in electro-mechanical machinery from vibration measurements using one-class support vector machines (SVMs). In order to evaluate one-class SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data. <s> BIB014 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> This paper investigates the use of machine learning classification techniques applied to the task of recognising the make and model of vehicles. Although a number of vehicle classification systems already exist, most of them seek only to distinguish between vehicle categories, e.g. identifying whether a vehicle is a bus, truck or car. The system presented here demonstrates that a set of features extracted from the frontal view of a vehicle may be used to determine the vehicle type (make and model) with high accuracy. The performance of some standard multi-class classification algorithms is compared for this problem. A one-class k-Nearest Neighbour classification algorithm is also implemented and tested. <s> BIB015 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> This paper describes an application of one-class support vector machine (SVM) novelty detection for detecting seizures in humans. Our technique maps intracranial electroencephalogram (EEG) time series into corresponding novelty sequences by classifying short-time, energy-based statistics computed from one-second windows of data. We train a classifier on epochs of interictal (normal) EEG. During ictal (seizure) epochs of EEG, seizure activity induces distributional changes in feature space that increase the empirical outlier fraction. A hypothesis test determines when the parameter change differs significantly from its nominal value, signaling a seizure detection event. Outputs are gated in a .one-shot. manner using persistence to reduce the false alarm rate of the system. The detector was validated using leave-one-out cross-validation (LOO-CV) on a sample of 41 interictal and 29 ictal epochs, and achieved 97.1% sensitivity, a mean detection latency of -7.58 seconds, and an asymptotic false positive rate (FPR) of 1.56 false positives per hour (Fp/hr). These results are better than those obtained from a novelty detection technique based on Mahalanobis distance outlier detection, and comparable to the performance of a supervised learning technique used in experimental implantable devices (Echauz et al., 2001). The novelty detection paradigm overcomes three significant limitations of competing methods: the need to collect seizure data, precisely mark seizure onset and offset times, and perform patient-specific parameter tuning for detector training. <s> BIB016 </s> A survey of recent trends in one class classification <s> Application Domain Used <s> Unsupervised or unlabeled learning approaches for network anomaly detection have been recently proposed. In particular, recent work on unlabeled anomaly detection focused on high speed classification based on simple payload statistics. For example, PAYL, an anomaly IDS, measures the occurrence frequency in the payload of n-grams. A simple model of normal traffic is then constructed according to this description of the packets' content. It has been demonstrated that anomaly detectors based on payload statistics can be "evaded" by mimicry attacks using byte substitution and padding techniques. In this paper we propose a new approach to construct high speed payload-based anomaly IDS intended to be accurate and hard to evade. We propose a new technique to extract the features from the payload. We use a feature clustering algorithm originally proposed for text classification problems to reduce the dimensionality of the feature space. Accuracy and hardness of evasion are obtained by constructing our anomaly-based IDS using an ensemble of one-class SVM classifiers that work on different feature spaces. <s> BIB017
Text Classification. Traditional text classification techniques require an appropriate distribution of positive and negative examples to build a classifier; thus they are not suitable for this problem of OCC. It is of course possible to manually label some negative examples, though that it is labour-intensive and a time consuming task. However the core problem remains, that it is difficult or impossible to compile a set of negative samples that provides a comprehensive characterization of everything that is 'not' the target concept, as is assumed by a conventional binary classifier. The ability to build classifiers without negative training data is useful in a scenario when one needs to extract positive documents from many text collections or sources. Liu et al. BIB004 propose a method (called Spy EM) to solve this problem in the text domain. It is based on Naïve Bayesian classification (NB) and the Expectation Maximization (EM) algorithm BIB001 . The main idea of the method is to first use a technique to identify some reliable / strong negative documents from the unlabeled set. It then runs EM to build the final classifier. Yu et al. BIB005 [31] propose an SVM-based technique called PEBL (Positive Example Based Learning) to classify Web pages with positive and unlabeled pages. Once a set of strong negative documents is identified, SVM is applied iteratively to build a classifier. PEBL is sensitive to the number of positive examples and gives poor results when they are small in number. Li and Liu  propose an alternative method that extracts negative data from the unlabeled set using the Rocchio method  . Although the second step also runs SVM iteratively to build a classifier, there is a key difference in selection of the final classifier. Their technique selects a "good" classifier from a set of classifiers built by SVM, while PEBL does not. It is shown theoretically by Liu et al. BIB004 that if the sample size is large enough, maximizing the number of unlabeled examples classified as negative while constraining the positive examples to be correctly classified will produce a good classifier. Liu et al. BIB006 develop a benchmarking system called LPU (Learning from Positive and Unlabeled data) BIB005 and also propose an ap-proach based on a biased formulation of SVM that allows noise (or error) in positive examples. They experiment on Reuters data set and Usenet articles by Lang  and conclude that biased-SVM approach outperforms other existing two-step techniques. Peng et al.  present a text classifier from positive and unlabeled documents based on Genetic Algorithms (GA). They perform experiments on the Reuters data set and compare their results against PEBL BIB010 and OSVM, and show that their GA based classification performs better. Koppel et al. BIB011 study the "Authorship Verification" problem where only examples of writings of a single author are given and the task is to determine if a given piece of text is or is not written by this author. They test their algorithm on a collection of twentyone 19th century English books written by 10 different authors and spanning a variety of genres. They obtain overall accuracy of 95.7% with errors almost equally distributed between false positives and false negatives. Denis et al.  introduce a Naïve Bayes algorithm and shows its feasibility for learning from positive and unlabeled documents. The key step in their method is in estimating word probabilities for the negative class because negative examples were not available. This limitation can be overcome by assuming an estimate of the positive class probability (the ratio of positive documents in the set of all documents). In practical situations, the positive class probability can be empirically estimated or provided by domain knowledge. Their results on the WebKB data set 4 show that error rates of Naïve Bayes classifiers obtained from positive examples trained with enough unlabeled examples are lower than error rates of Naïve Bayes classifiers obtained from labeled documents. Other Application Domains. OSVMs have been successfully applied in a wide variety of application domains such as Handwritten Digit Recognition BIB002 [2]  , Information Retrieval BIB003 , Face Recognition Applications BIB012 [37], Medical Analysis BIB016 , Bioinformatics BIB013  , Spam Detection  , Anomaly Detection BIB007 BIB017 , Machine Fault Detection BIB014 . Compression neural networks for one-sided classification have been used in many application areas, including detecting Mineral Deposits BIB008 . Wang and Stolfo use one-class Naïve Bayes to detect Masquerade Detection BIB009 in a network and show that less effort in data collection is required with comparable performance as that of a multi-class classifier. Munroe and Madden BIB015 present a one class k-nearest neighbor approach for vehicle recognition from images and showed that the results are comparable to that of standard multi-class classifiers.