Fuzzy ILP Classification of web reports after linguistic text mining

In this paper we study the problem of classification of textual web reports. We are specifically focused on situations in which structured information extracted from the reports is used for classification. We present an experimental classification system based on usage of third party linguistic analyzers, our previous work on web information extraction, and fuzzy inductive logic programming (fuzzy ILP). A detailed study of the so-called ‘Fuzzy ILP Classifier’ is the main contribution of the paper. The study includes formal models, prototype implementation, extensive evaluation experiments and comparison of the classifier with other alternatives like decision trees, support vector machines, neural networks, etc.

Research highlights
► Two classification methods based on ILP were implemented: Fuzzy and Crisp.
► Fuzzy method outperformed the Crisp one in the evaluation.
► Fuzzy method is suitable for small training sets and monotonically ordered domains.
► On larger training sets not significant improvement to the state of the art provided.