Best terms: an efficient feature-selection algorithm for text categorization
In this paper, we propose a new feature-selection algorithm for text classification, called best terms (BT). The complexity of BT is linear in respect to the number of the training-set documents and is independent from both the vocabulary size and the number of categories. We evaluate BT on two benchmark document collections, Reuters-21578 and 20-Newsgroups, using two classification algorithms, naive Bayes (NB) and support vector machines (SVM). Our experimental results, comparing BT with an extensive and representative list of feature-selection algorithms, show that (1) BT is faster than the existing feature-selection algorithms; (2) BT leads to a considerable increase in the classification accuracy of NB and SVM as measured by the F1 measure; (3) BT leads to a considerable improvement in the speed of NB and SVM; in most cases, the training time of SVM has dropped by an order of magnitude; (4) in most cases, the combination of BT with the simple, but very fast, NB algorithm leads to classification accuracy comparable with SVM while sometimes it is even more accurate.
text categorization feature selection machine learning