Multi-evidence, multi-criteria, lazy associative document classification

We present a novel approach for classifying documents that combines different pieces of evidence (e.g., textual features of documents, links, and citations) transparently, through a data mining technique which generates rules associating these pieces of evidence to predefined classes. These rules can contain any number and mixture of the available evidence and are associated with several quality criteria which can be used in conjunction to choose the "best" rule to be applied at classification time. Our method is able to perform evidence enhancement by link forwarding/backwarding (i.e., navigating among documents related through citation), so that new pieces of link-based evidence are derived when necessary. Furthermore, instead of inducing a single model (or rule set) that is good on average for all predictions, the proposed approach employs a lazy method which delays the inductive process until a document is given for classification, therefore taking advantage of better qu...

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Adriano Veloso, Wagner Meira Jr., Marco Cristo, Marcos André Gonçalves, Mohammed Javeed Zaki
• Keywords: Available Evidence, Better Qualitative Evidence, CIKM 2006, Documents, Information Management