Integration of Autoencoder and Functional Link Artificial Neural Network for Multi-label Classification
This work addresses the problem of complex decision boundaries in multi-label classification for researchers, but it is incremental as it builds on existing methods like functional link neural networks and autoencoders.
The authors tackled multi-label classification by developing a neural network that combines functional expansion and autoencoder transformations to improve separability and reduce feature space, achieving superior performance on five datasets compared to six established classifiers.
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.