A feature mining based approach for the classification of text documents into disjoint classes
This paper proposes a new approach for classifying text documents into two disjoint classes. The new approach is based on extracting patterns, in the form of two logical expressions, which are defined on various features (indexing terms) of the documents. The pattern extraction is aimed at providing descriptions (in the form of two logical expressions) of the two classes of positive and negative examples. This is achieved by means of a data mining approach, called One Clause At a Time (OCAT), which is based on mathematical logic. The application of a logic-based approach to text document classification is critical when one wishes to be able to justify why a particular document has been assigned to one class versus the other class. This situation occurs, for instance, in declassifying documents that have been previously considered important to national security and thus are currently being kept as secret. Some computational experiments have investigated the effectiveness of the OCAT-based approach and compared it to the well-known vector space model (VSM). These tests also have investigated finding the best indexing terms that could be used in making these classification decisions. The results of these computational experiments on a sample of 2897 text documents from the TIPSTER collection indicate that the first approach has many advantages over the VSM approach for solving this type of text document classification problem. Moreover, a guided strategy for the OCAT-based approach is presented for deciding which document one needs to consider next while building the training example sets.
data mining document classification vector space model document indexing one clause at a time (ocat) algorithm machine learning