We present a supervised learning algorithm for text categorization which has brought the team of authors the 2nd place in the text categorization division of the 2012 Cybersecurity Data Mining Competition (CDMC'2012) and a 3rd prize overall. The algorithm is quite different from existing approaches in that it is based on similarity search in the metric space of measure distributions on the dictionary. At the preprocessing stage, given a labeled learning sample of texts, we associate to every class label (document category) a point in the space of question. Unlike it is usual in clustering, this point is not a centroid of the category but rather an outlier, a uniform measure distribution on a selection of domain-specific words. At the execution stage, an unlabeled text is assigned a text category as defined by the closest labeled neighbour to the point representing the frequency distribution of the words in the text. The algorithm is both effective and efficient, as further confirmed by experiments on the Reuters 21578 dataset.
@cite_0: The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
From this section onwards, following notation similar to @cite_0 , we let @math denote the dataset of documents, with size @math , and @math the data dictionary of all unique words from documents in @math , with size @math . Given a document @math and a word @math , @math denotes the number of words in @math and @math indicates that the word @math is found in @math .