Title: A Cost-Sensitive Deep Belief Network for Imbalanced Classification,
Abstract: Imbalanced data with a skewed class distribution are common in many
real-world applications. Deep Belief Network (DBN) is a machine learning
technique that is effective in classification tasks. However, conventional DBN
does not work well for imbalanced data classification because it assumes equal
costs for each class. To deal with this problem, cost-sensitive approaches
assign different misclassification costs for different classes without
disrupting the true data sample distributions. However, due to lack of prior
knowledge, the misclassification costs are usually unknown and hard to choose
in practice. Moreover, it has not been well studied as to how cost-sensitive
learning could improve DBN performance on imbalanced data problems. This paper
proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for
imbalanced classification. ECS-DBN uses adaptive differential evolution to
optimize the misclassification costs based on training data, that presents an
effective approach to incorporating the evaluation measure (i.e. G-mean) into
the objective function. We first optimize the misclassification costs, then
apply them to deep belief network. Adaptive differential evolution optimization
is implemented as the optimization algorithm that automatically updates its
corresponding parameters without the need of prior domain knowledge. The
experiments have shown that the proposed approach consistently outperforms the
state-of-the-art on both benchmark datasets and real-world dataset for fault
diagnosis in tool condition monitoring.