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

  • Authors:

  • Adriano Veloso;Wagner Meira, Jr.;Marco Cristo;Marcos Gonçalves;Mohammed Zaki

  • Affiliations:

  • Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;Rensselaer Polytechnic Institute, Troy

  • Venue:
  • CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
  • Year:
  • 2006

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Abstract

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 qualitative evidence coming from the document. We conducted a systematic evaluation of the proposed approach using documents from the ACM Digital Library and from a Brazilian Web directory. Our approach was able to outperform in both collections all classifiers based on the best available evidence in isolation as well as state-of-the-art multi-evidence classifiers. We also evaluated our approach using the standard WebKB collection, where our approach showed gains of 1% in accuracy, being 25 times faster. Further, our approach is extremely efficient in terms of computational performance, showing gains of more than one order of magnitude when compared against other multi-evidence classifiers.