Learning the Sub-Conceptual Layer: A Framework for One-Class Classification

In the realm of machine learning research and application, binary classification algorithms, i.e. algorithms that attempt to induce discriminant functions between two categories of data, reign supreme. Their fundamental property is the reliance on the availability of data from all known categories in order to induce functions that can offer acceptable levels of accuracy. Unfortunately, data from so-called ``real-world'' domains sometimes do not satisfy this property. In order to tackle this, researchers focus on methods such as sampling and cost-sensitive classification to make the data more conducive for binary classifiers.\ud However, as this thesis shall argue, there are scenarios in which even such explicit methods to rectify distributions fail. In such cases, one-class classification algorithms become a practical alternative. Unfortunately, if the domain is inherently complex, the advantage that they offer over binary classifiers becomes diminished. The work in this thesis addresses this issue, and builds a framework that allows for one-class algorithms to build efficient classifiers.\ud In particular, this thesis introduces the notion of learning along the lines sub-concepts in the domain; the complexity in domains arises due to the presence of sub-concepts, and by learning over them explicitly rather than on the entire domain as a whole, we can produce powerful one-class classification systems. The level of knowledge regarding these sub-concepts will naturally vary by domain, and thus we develop three distinct frameworks that take the amount of domain knowledge available into account. We demonstrate these frameworks over three real-world domains.\ud The first domain we consider is that of biometric authentication via a users swipe on a smartphone. We identify sub-concepts based on a users motion, and given that modern smartphones employ sensors that can identify motion, during learning as well as application, sub-concepts can be identified explicitly, and novel instances can be processed by the appropriate one-class classifier. The second domain is that of invasive isotope detection via gamma-ray spectra. The sub-concepts are based on environmental factors; however, the hardware employed cannot detect such concepts, and quantifying the precise source that creates these sub-concepts is difficult to ascertain. To remedy this, we introduce a novel framework in which we employ a sub-concept detector by means of a multi-class classifier, which pre-processes novel instances in order to send them to the correct one-class classifier. The third domain is that of compliance verification of the Comprehensive Test Ban Treaty (CTBT) through Xenon isotope measurements. This domain presents the worst case where sub-concepts are not known. To this end, we employ a generic version of our framework in which we simply cluster the domain and build classifiers over each cluster. In all cases, we demonstrate that learning in the context of domain concepts greatly improves the performance of one-class classifiers