When scanning documents with a large number of pages such as books, it is often feasible to provide a minimal number of training samples to personalize the system to compensate fo...
Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decisi...
This paper presents research results of our investigation of the imbalanced data problem in the classification of different types of weld flaws, a multi-class classification probl...
In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Cor...