  The challenge of imbalanced learning lies not only in class imbalance
problem, but also in the class overlapping problem which is complex. However,
most of the existing algorithms mainly focus on the former. The limitation
prevents the existing methods from breaking through. To address this
limitation, this paper proposes an ensemble learning algorithm based on dual
clustering and stage-wise hybrid sampling (DCSHS). The DCSHS has three parts.
Firstly, we design a projection clustering combination framework (PCC) guided
by Davies-Bouldin clustering effectiveness index (DBI), which is used to obtain
high-quality clusters and combine them to obtain a set of cross-complete
subsets (CCS) with balanced class and low overlapping. Secondly, according to
the characteristics of subset classes, a stage-wise hybrid sampling algorithm
is designed to realize the de-overlapping and balancing of subsets. Finally, a
projective clustering transfer mapping mechanism (CTM) is constructed for all
processed subsets by means of transfer learning, thereby reducing class
overlapping and explore structure information of samples. The major advantage
of our algorithm is that it can exploit the intersectionality of the CCS to
realize the soft elimination of overlapping majority samples, and learn as much
information of overlapping samples as possible, thereby enhancing the class
overlapping while class balancing. In the experimental section, more than 30
public datasets and over ten representative algorithms are chosen for
verification. The experimental results show that the DCSHS is significantly
best in terms of various evaluation criteria.
