Multi Label Ranking Based on
Positive
Pairwise Correlations Among Labels
Raed Alazaidah, Farzana Ahmad, and Mohamad Mohsin
School of Computing, Universiti Utara Malaysia,
Malaysia
Abstract: Multi-Label
Classification (MLC) is a general type of classification that has attracted
many researchers in the last few years. Two common approaches are being used to
solve the problem of MLC: Problem Transformation Methods (PTMs) and Algorithm Adaptation
Methods (AAMs). This Paper is more interested in the first approach; since it
is more general and applicable to any domain. In specific, this paper aims to
meet two objectives. The first objective is to propose a new multi-label
ranking algorithm based on the positive pairwise correlations among labels,
while the second objective aims to propose new simple PTMs that are based on
labels correlations, and not based on labels frequency as in conventional PTMs.
Experiments showed that the proposed algorithm overcomes the existing methods
and algorithms on all evaluation metrics that have been used in the
experiments. Also, the proposed PTMs show a superior performance when compared
with the existing PTMs.
Keywords: Correlations among
labels, multi-label classification, multi-label ranking, problem transformation
methods.
Received
August 23, 2017; accepted August 2, 2018