  Multi-label (ML) classification is an actively researched topic currently,
which deals with convoluted and overlapping boundaries that arise due to
several labels being active for a particular data instance. We propose a
classifier capable of extracting underlying features and introducing
non-linearity to the data to handle the complex decision boundaries. A novel
neural network model has been developed where the input features are subjected
to two transformations adapted from multi-label functional link artificial
neural network and autoencoders. First, a functional expansion of the original
features are made using basis functions. This is followed by an
autoencoder-aided transformation and reduction on the expanded features. This
network is capable of improving separability for the multi-label data owing to
the two-layer transformation while reducing the expanded feature space to a
more manageable amount. This balances the input dimension which leads to a
better classification performance even for a limited amount of data. The
proposed network has been validated on five ML datasets which shows its
superior performance in comparison with six well-established ML classifiers.
Furthermore, a single-label variation of the proposed network has also been
formulated simultaneously and tested on four relevant datasets against three
existing classifiers to establish its effectiveness.
