text classification using esc-based stochastic decision lists
We propose a new method of text classification using stochastic decision lists. A stochastic decision list is an ordered sequence of IF-THEN rules, and our method can be viewed as a rule-based method for text classification having advantages of readability and refinability of acquired knowledge. Our method is unique in that decision lists are automatically constructed on the basis of the principle of minimizing Extended Stochastic Complexity (ESC), and with it we are able to construct decision lists that have fewer errors in classification. The accuracy of classification achieved with our method appears better than or comparable to those of existing rule-based methods.
text classification classification method sequence rules readability knowledge complexity errors accuracy