Abstract:
In real world applications, it is very common to encounter data with high class
imbalance. Imbalanced dataset is a challenging issue in practical classi cation
problem, as the classi er gets biased towards the majority classes.
The traditional techniques like synthetic minority oversampling have great success
in traditional machine learning problems with class imbalance, however these
techniques fail to perform well in the eld of complex, structured and very high
dimensional data like images.
In our work we propose a novel dynamic oversampling framework, which is broadly
subdivided into three parts. The rst step is the representation learning of the
dataset, where a Convolutional Neural Network is used to map the raw input
training data into a new feature space. In the second step a modi ed minority
oversampling technique is implemented with adaptive k-NN based search between
in-class samples in deep feature space. Finally a dense neural classi er is trained on
the augmented dataset. To increase the discriminating power of the nal classi er
we have trained it with modi ed sample weights.
We have also supplemented our work with empirical studies on publicly available
benchmark image datasets and have shown that our technique provides a
good countermeasure to handle imbalanced image datasets and provides superior
performance than existing techniques.
