Abstract:
Classifying a multiclass data set with an imbalanced distribution of class repre-
sentatives in the data set is a challenging problem which is prevalent in many
real-world applications. In this study,we have made a comparative analysis of
di erent decomposition techniques like OneVsAll(OVA), OneVsOne(OVO), Error
Correcting Output Codes(ECOC), All-and-One(A&O) and One-Against-Lower-
Order(OALO) to deal with the multiclass imbalance. While OVA and OVO have
been used signi cantly in the multiclass imbalance domain, our work is the rst to
explore the remaining binarization approaches in this eld. We have examined the
performance of these decomposition methods on two types of learning : algorith-
mic approach and hybrid approach of both data-level and algorithmic solutions to
solve the binary class imbalance classi cation problem. For the algorithmic ap-
proach learning we have used Hellinger Distance Decision Trees and for the hybrid
method, we propose Balanced Ensemble Models (BEM) that combines both sam-
pling and algorithm level modi cations. It has been analyzed how e ectively the
decomposition methods when applied on our approach can counter the challenges
of multiclass imbalance. A detailed experimental study, supported by statistical
analysis has been carried out to determine which combination of classi er(between
HDDT and our proposed ensemble method) and decomposition scheme work best
to produce satisfactory classi cation performance on a multiclass imbalanced data
set. From our research we conclude that ECOC decomposition strategy when ap-
plied on our proposed BEM outperforms all the other algorithms in dealing with
multiclass imbalance problem.
