SRFW: a simple, fast and effective text classification algorithm

Text classification is a powerful technique for automating assignment of documents to topic hierarchies. Although there are a number of text classification algorithms, most of them are either inefficient or too complex. We present a linear text classification algorithm called SRFW, which is fast, effective and easily used. SRFW obtains relevance factors. For new unlabelled documents, SRFW adopts sum of weights based on relevance factors to obtain the probability that these documents belong to each category and assigns them to categories that have the biggest probability. We have evaluated our algorithm on a subset of Reuters-21578 and 20-newsgroups text collections and compared it against k-NN and SVM. Experimental results show that SRFW is competitive with k nearest neighbor (k-NN) and support vector machines (SVM), while SRFW is much simpler and faster than them.

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