Text classification (also known as text categorization, or topic spotting) is the task of automatically sorting a set of documents into categories from a predefined set. This task has several applications, including automated indexing of scientific articles according to predefined thesauri of technical terms, filing patents into patent directories, selective dissemination of information to information consumers, automated population of hierarchical catalogues of Web resources, spam filtering, identification of document genre or authorship attribution. Automated text classification is attractive because it frees organizations from the need of manually organizing document bases, which can be too expensive, or simply not feasible given the time constraints of the application or the number of documents involved.

This section shows the Text Classification data sets avalaible in the repository. Please, note that data sets found here are not the original ones. We have applied a pre-processing technique which divides the original data set into a collection of N binary ones (one for each class label of the original dataset). Each one of these binary data sets consists of 100 attributes selected by using Chi Square scoring function.

Thus, every data set defines a supervised classification problem, where each of its examples is composed by some nominal attributes and a label indicating the example's class.

Each data file has the following structure:

• @relation: Name of the data set
• @attribute: Description of an attribute (one for each attribute)
• @inputs: List with the names of the input attributes
• @output: List with the names of the output attributes
• @data: Starting tag of the data

The rest of the file contains all the examples belonging to the data set, expressed in comma sepparated values format.