Using Error-Correcting Codes for Text Classification

This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text classification tasks can be significantly improved by taking advantage of the error-correcting properties of the code. We also explore the use of different kinds of codes, namely Error-Correcting Codes, Random Codes, and Domain and Data-specific codes and give experimental results for each of them. The ECOC method scales well to large data sets with a large number of classes. Experiments on a real-world data set show a reduction in classification error by up to 66% over the traditional Naive Bayes Classifier. We also compare our empirical results to semitheoretical results and find that the two closely agree.

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Rayid Ghani
• Keywords: Error Correcting Output, Error-Correcting Codes, ICML 2000, Machine Learning, Traditional Naive Bayes