Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Using Error-Correcting Codes for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Question classification with support vector machines and error correcting codes
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-driven decomposition for multi-class classification
Pattern Recognition
Enhancing the Performance of Centroid Classifier by ECOC and Model Refinement
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Error-correcting output codes: a general method for improving multiclass inductive learning programs
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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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.