Affiliations: Department of Electrical, Computer, and Software Engineering, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario L1H 7K4, Canada. E-mail: [email protected]
Abstract: One of the major tasks in text categorization systems is
dimensionality reduction, which strongly affects classification
performance and scalability. Among dimensionality reduction methods,
feature ranking-based feature selection, also known as best
individual features, is scalable, simple, and inexpensive.
However, selecting the proper feature ranking method for a given
data set is not obvious without conducting experiments on the given
data set. The performance varies depending on the data
characteristics and the choice of the classifier. In this paper a
framework, which is called feature meta-ranking, is introduced to
identify the best feature ranking measure among a set of candidate
solutions for a particular text classification problem. The feature
meta-ranking technique is implemented based on the differential
filter level performance method. This method uses a simple
classifier, such as Rocchio, to estimate the behavior of the feature
ranking measure with respect to a particular data set. With respect
to the use of a classifier in the feature selection loop, the
proposed method can be considered as a hybrid feature selection
technique with minimal use of a classifier in the loop. The proposed
method is evaluated by applying it to six data sets. Seven feature ranking measures are employed and evaluated. The stability of the method in terms of
insensitivity to the resolution of filter level is demonstrated. The
proposed method is also examined with more sophisticated classifiers
such as support vector machines, and the results confirm the
performance obtained with simple classifiers.
Keywords: Feature selection, supervised learning, text classification
