ABSTRACT
The ranking problem appears in many areas of study such
as customer rating, social science, economics, and information
retrieval. Ranking can be formulated as a classification
problem when pair-wise data is considered. However this
approach increases the problem complexity from linear to
quadratic in terms of sample size. We present in this paper
a convex hull reduction method to reduce this impact. We
also propose a 1-norm regularization approach to simultaneously
find a linear ranking function and to perform feature
subset selection. The proposed method is formulated as a
linear program. We present experimental results on artificial
data and two real data sets, concrete compressive strength
data set and Abalone data set.