A survey of recent trends in one class classification <s> Introduction <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> BIB001 </s> A survey of recent trends in one class classification <s> Introduction <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> BIB002
Conventional multi-class classification algorithms aim to classify an unknown object into one of several pre-defined categories. A problem arises when the unknown object does not belong to any of those categories. In one-class classification BIB001  , one of the classes (referred to as the positive class or target class) is well characterized by instances in the training data. For the other class (nontarget), it has either no instances at all, very few of them, or they do not form a statistically-representative sample of the negative concept. To motivate the importance of one-class classification, let us consider some scenarios. One-class classification can be relevant in detecting machine faults, for instance. A classifier should detect when the machine is showing abnormal/faulty behaviour. Measurements on the normal operation of the machine (positive class training data) are easy to obtain. On the other hand, most faults will not have occurred so one will have little or no training data for the negative class. As another example, a traditional binary classifier for text or web pages requires arduous pre-processing to collect negative training examples. For example, in order to construct a homepage classifier BIB002 , collecting sample of homepages (positive training examples) is relatively easy, however collecting samples of nonhomepages (negative training examples) is very challenging because it may not represent the negative concept uniformly and may involve human bias. The outline of the paper is as follows. In Section 2 we compare OCC and multi-class classification problems. In Section 3 we propose a taxonomy for the study of OCC and present current state-of-the-art survey of some of the major research contributions under the proposed taxonomy. Section 4 concludes our presentation with summary of the research work in OCC and guidelines for future research.