Resumen de Text Categorization Using a New Text Association Rule-Based Classifier
Supaporn Buddeewong, Worapoj Kreesurakej
This paper proposes a new association rule-based Classifier algorithm to improve the prediction accuracy of Association Rule-based Classifier By Categories (ARC-BC) algorithm. Unlike the previous algorithms, the proposed association rule generation algorithm constructs two types of frequent itemsets. The first frequent itemsets, i.e. Lk, contain all term that have no an overlap with other categories. The second frequent itemsets, i.e. OLk, contain all features that have an overlap with other categories. In addition, this paper also proposes a new join operation for the second frequent itemsets. The experimental results are shown a good performance of the proposed classifier.
