Error-Correcting Output Coding (ECOC) is a general framework for multiclass text classification with a set of binary classifiers. It can not only help a binary classifier solve multi-class classification problems, but also boost the performance of a multi-class classifier. When building each individual binary classifier in ECOC, multiple classes are randomly grouped into two disjoint groups: positive and negative. However, when training such a binary classifier, sub-class distribution within positive and negative classes is neglected. Utilizing this information is expected to improve a binary classifier. We thus design a simple binary classification strategy via multi-class categorization (2vM) to make use of sub-class partition information, which can lead to better performance over the traditional binary classification. The proposed binary classification strategy is then applied to enhance ECOC. Experiments on document categorization and question classification show its effectiveness. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, B., & Vogel, C. (2010). Improving multiclass text classification with error-correcting output coding and sub-class partitions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6085 LNAI, pp. 4–15). https://doi.org/10.1007/978-3-642-13059-5_4
Mendeley helps you to discover research relevant for your work.