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.
The standard approach in literature for text categorization is that one, or more, feature selection or extraction technique is first applied to a data matrix, since the original data matrix is often extremely high-dimensional. A learning algorithm, independent of the dimension reduction process, is then used for classification @cite_0 . The novel approach in this paper is that we consider a new classifier based only on extracted class specific words, which naturally reduces time complexity and the dimension of the dataset. In other words, the Domain-Specific classifier both performs dimension reduction and classifies, in consecutive and dependent steps.
@cite_0
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In this paper, we consider the task of text categorization as a graph classification problem. By representing textual documents as graph-of-words instead of historical n-gram bag-of-words, we extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept of k-core, we reduce the graph representation to its densest part – its main core – speeding up the feature extraction step for little to no cost in prediction performances. Experiments on four standard text classification datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baseline approaches. Text categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones. In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appropriate in the case of many digital libraries that offer as contents scanned and optically read books or magazines. In this paper, we propose a more general formulation of text categorization, allowing documents to be organized as sequences of pages. We introduce a novel hybrid system specifically designed for multi-page text documents. The architecture relies on hidden Markov models whose emissions are bag-of-words resulting from a multinomial word event model, as in the generative portion of the Naive Bayes classifier. The rationale behind our proposal is that taking into account contextual information provided by the whole page sequence can help disambiguation and improves single page classification accuracy. Our results on two datasets of scanned journals from the Making of America collection confirm the importance of using whole page sequences. The empirical evaluation indicates that the error rate (as obtained by running the Naive Bayes classifier on isolated pages) can be significantly reduced if contextual information is incorporated. Text categorization is the classification to assign a text document to an appropriate category in a predefined set of categories. We focus on the special case when categories are organized in hierarchy. We present a new approach on this recently emerged subfield of text categorization. The algorithm applies an iterative learning module that allow of gradually creating a classifier by trial-and-error-like method. We present a software that has been developed on the basis of the algorithm to illustrate the capability of the algorithm on large data collection. We experimented on the very large benchmark collection, on the WIPO-alpha (World Intellectual Property Organization, Geneva, Switzerland, 2002) English patent database that consists of about 75000 XML documents distributed over 5000 categories. Our software is able to index the corpus quickly and creates a classifier in a few iteration cycles. We present the results achieved by the classifier w.r.t. various test setting