Intelligent Fusion of Evidence from Multiple Sources
for Text Classification
Virginia Polytechnic Institute and State University, USA, Doctor of Philosophy in Computer Science and
Applications, (September 2006)Abstract
Automatic text classification using current approaches
is known to perform poorly when documents are noisy or
when limited amounts of textual content is available.
Yet, many users need access to such documents, which
are found in large numbers in digital libraries and in
the WWW. If documents are not classified, they are
difficult to find when browsing. Further, searching
precision suffers when categories cannot be checked,
since many documents may be retrieved that would fail
to meet category constraints. In this work, we study
how different types of evidence from multiple sources
can be intelligently fused to improve classification of
text documents into predefined categories. We present a
classification framework based on an inductive learning
method -- Genetic Programming (GP) -- to fuse evidence
from multiple sources. We show that good classification
is possible with documents which are noisy or which
have small amounts of text (e.g., short metadata
records) -- if multiple sources of evidence are fused
in an intelligent way. The framework is validated
through experiments performed on documents in two
testbeds. One is the ACM Digital Library (using a
subset available in connection with CITIDEL, part of
NSF's National Science Digital Library). The other is
Web data, in particular that portion associated with
the Cadê Web directory. Our studies have shown that
improvement can be achieved relative to other machine
learning approaches if genetic programming methods are
combined with classifiers such as kNN. Extensive
analysis was performed to study the results generated
through the GP-based fusion approach and to understand
key factors that promote good classification.
Links and resources
Tags
community
@brazovayeye's tags highlighted