  When the training data in a two-class classification problem is overwhelmed
by one class, most classification techniques fail to correctly identify the
data points belonging to the underrepresented class. We propose
Similarity-based Imbalanced Classification (SBIC) that learns patterns in the
training data based on an empirical similarity function. To take the imbalanced
structure of the training data into account, SBIC utilizes the concept of
absent data, i.e. data from the minority class which can help better find the
boundary between the two classes. SBIC simultaneously optimizes the weights of
the empirical similarity function and finds the locations of absent data
points. As such, SBIC uses an embedded mechanism for synthetic data generation
which does not modify the training dataset, but alters the algorithm to suit
imbalanced datasets. Therefore, SBIC uses the ideas of both major schools of
thoughts in imbalanced classification: Like cost-sensitive approaches SBIC
operates on an algorithm level to handle imbalanced structures; and similar to
synthetic data generation approaches, it utilizes the properties of unobserved
data points from the minority class. The application of SBIC to imbalanced
datasets suggests it is comparable to, and in some cases outperforms, other
commonly used classification techniques for imbalanced datasets.
