One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. Generally, pattern recognition systems designed by a single source of information suffer from limitations such as the lack of uniqueness and nonuniversality. Thus, combining information from multiple sources becomes a mode for designing pattern recognition systems. Usually, fixed rules such as average, product, minimum and maximum are the standard used combiners for OCC ensembles. However, fixed combiners cannot be useful to treat some difficult cases. Hence, we propose in this paper a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Experimental results conducted on different types of OCC and two different handwritten datasets prove the superiority of FI against fixed combiners for an open multi-class classification based on OCC ensemble.
CITATION STYLE
Hadjadji, B., Chibani, Y., & Nemmour, H. (2014). Fuzzy integral combination of one-class classifiers designed for multi-class classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8814, pp. 320–328). Springer Verlag. https://doi.org/10.1007/978-3-319-11758-4_35
Mendeley helps you to discover research relevant for your work.