Text categorization (TC) is a task of assigning one or multiple predefined category labels to natural language texts. To deal with this sophisticated task, a variety of statistical classification methods and machine learning techniques have been exploited intensively (Sebastiani, 2002), including the Naïve Bayesian (NB) classifier (Lewis, 1998), the Vector Space Model (VSM)-based classifier (Salton, 1989), the example-based classifier (Mitchell, 1996), and the Support Vector Machine (Yang & Liu, 1999). Text filtering is a basic type of text categorization (two-class TC). There are many real-life applications (Fan, 2004), a typical one of which is the ill information filtering, such as erotic information and garbage information filtering on the web, in e-mails and in short messages of mobile phones. It is obvious that this sort of information should be carefully controlled. On the other hand, the filtering performance using the existing methodologies is still not satisfactory in general. The reason lies in that there exist a number of documents with high degree of ambiguity, from the TC point of view, in a document collection, that is, there is a fuzzy area across the border of two classes (for the sake of expression, we call the class consisting of the ill information- related texts, or, the negative samples, the category of TARGET, and, the class consisting of the ill information-not-related texts, or, the positive samples, the category of Non-TARGET). Some documents in one category may have great similarities with some other documents in the other category, for example, a lot of words concerning love story and sex are likely appear in both negative samples and positive samples if the filtering target is erotic information.
An Axiomatization of the Proportional Rule in Financial Networks
