On Reject and Refine Options in Multicategory Classification

Reject Options in Fuzzy Pattern Classification Rules

A pattern to be classified is an object described by d feature values, i.e. a d-dimensional vector x= (x1 x2 ... xd) . Conventional pattern classification aims at associating each pattern to one of c predefined classes {ω1, ω2, ..., ωc} assumed to be representative of particular objects available from a learning set Ω = {x1,x1, ...xn}. The classification is carried out on the base of class labe...

Optimum Reject Options for Prototype-based Classification

We analyse optimum reject strategies for prototype-based classifiers and real-valued rejection measures, using the distance of a data point to the closest prototype or probabilistic counterparts. We compare reject schemes with global thresholds, and local thresholds for the Voronoi cells of the classifier. For the latter, we develop a polynomial-time algorithm to compute optimum thresholds base...

On learning multicategory classification with sample queries

Consider the pattern recognition problem of learning multi-category classification from a labeled sample, for instance, the problem of learning character recognition where a category corresponds to an alphanumeric letter. The classical theory of pattern recognition assumes labeled examples appear according to the unknown underlying pattern-class conditional probability distributions where the p...

Classification with Reject Options in a Logical Framework: a fuzzy residual implication approach

In many classification problems, overlapping classes and outliers can significantly decrease a classifier performance. In this paper, we introduce the possibility of a given classifier to reject patterns either for ambiguity or for distance. From a set of typicality degrees for a pattern to be classified, we propose to use fuzzy implications to quantify the similarity of the degrees. A class-se...

Protein Folding Classification Through Multicategory Discrete SVM