An evaluation of statistical approach to text categorization

This paper focuses on a comparative evaluation of a wide-range of text categorization methods, including previously published results on the Reuters corpus and new results of additional experiments. A controlled study using three classiers, kNN, LLSF and WORD, was conducted to examine the impact of conguration variations in ve versions of Reuters on the observed performance of classiers. Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were signicantly aected by the inclusion of a large portion of unlabelled documents, mading those results dicult to interpret and leading to considerable confusions in the literature. Using the results evaluated on the other versions of Reuters which exclude the unlabelled documents, the performance of twelve methods are compared directly or indirectly. For indirect compararions, kNN, LLSF and WORD were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performance; except for a Naive Bayes approach, the other learning algorithms also performed relatively well