  In this paper we address imbalanced binary classification (IBC) tasks.
Applying resampling strategies to balance the class distribution of training
instances is a common approach to tackle these problems. Many state-of-the-art
methods find instances of interest close to the decision boundary to drive the
resampling process. However, under-sampling the majority class may potentially
lead to important information loss. Over-sampling also may increase the chance
of overfitting by propagating the information contained in instances from the
minority class. The main contribution of our work is a new method called ICLL
for tackling IBC tasks which is not based on resampling training observations.
Instead, ICLL follows a layered learning paradigm to model the data in two
stages. In the first layer, ICLL learns to distinguish cases close to the
decision boundary from cases which are clearly from the majority class, where
this dichotomy is defined using a hierarchical clustering analysis. In the
subsequent layer, we use instances close to the decision boundary and instances
from the minority class to solve the original predictive task. A second
contribution of our work is the automatic definition of the layers which
comprise the layered learning strategy using a hierarchical clustering model.
This is a relevant discovery as this process is usually performed manually
according to domain knowledge. We carried out extensive experiments using 100
benchmark data sets. The results show that the proposed method leads to a
better performance relatively to several state-of-the-art methods for IBC.
