A survey of recent trends in one class classification <s> OCC Vs Multi-Class Classification <s> Target recognition requires the ability to distinguish targets from non-targets, a capability called one-class generalization. Many neural network pattern classifiers fail as one-class classifiers because they use open decision boundaries. To function as one-class classifier, a neural network must have three types of generalization: within-class, between-class, and out-of-class. We discuss these three types of generalization and identify neural network architectures that meet these requirements. We have applied our one-class classifier ideas to the problem of automatic target recognition in synthetic aperture radar. We have compared three neural network algorithms: Carpenter and Grossberg`s algorithmic version of the Adaptive Resonance Theory (ART-2A), Kohonen`s Learning Vector Quantization (LVQ), and Reilly and Cooper`s Restricted Coulomb Energy network (RCE). The ART 2-A neural network gives the best results, with 100% within-class, between-class, and out-of-class generalization. Experiments show that the network`s performance is sensitive to vigilance and number of training set presentations. <s> BIB001 </s> A survey of recent trends in one class classification <s> OCC Vs Multi-Class Classification <s> Abstract We propose a heuristic method of parameter estimation in mixture models for data with outliers and design a Bayesian classifier for assignment of m objects to n ⩾ m classes under constraints. This method of outlier handling combined with the classifier is applied to the well-known problem of automatic, constrained classification of chromosomes into their biological classes. We show that it decreases the error rate relative to the classical, normal, model by more than 50%. When applied to the Edinburgh feature data of the large Copenhagen image data set Cpr our best classifier yields an error rate close to 1.3% relative to chromosomes; 4 out of 5 cells are correctly classified. <s> BIB002
In a conventional multi-class classification problem, data from two (or more) classes are available and the decision boundary is supported by the presence of example samples from each class. Moya et al. BIB001 originate the term One-Class Classification in their research work. Different researchers have used other terms to present similar concepts such as Outlier Detection BIB002 , Novelty Detection  or Concept Learning  . These terms originate as a result of different applications to which OCC has been applied. The drawbacks that are encountered in the conventional classification problems, such as the estimation of error rates, measuring the complexity of a solution, the curse of dimensionality, the generalization of the method, and so on, also appear in OCC, and sometimes become even more prominent. As stated earlier, in OCC tasks, the negative data is either absent or limited in its distribution, so only one side of the classification boundary can be determined definitively by using the data. This makes problem of one-class classification harder than the problem of conventional multi-class / binary classification. The task in OCC is to define a classification boundary around the positive (or target) class, such that it accepts as many objects as possible from the positive class, while it minimizes the chance of accepting non-positive (or outlier) objects. Since only one side of the boundary can be determined, in OCC, it is hard to decide, on the basis of just one class how tightly the boundary should fit in each of the directions around the data. It is also harder to decide which attributes should be used to find the best separation of the positive and non-positive class objects. In particular, when the boundary of the data is long and non-convex, the required number of training objects might be very high. Hence it is to be expected that one-class classification algorithms will require a larger number training instances relative to conventional multi-class classification algorithms  .