  Multi-label classification is an approach which allows a datapoint to be
labelled with more than one class at the same time. A common but trivial
approach is to train individual binary classifiers per label, but the
performance can be improved by considering associations within the labels. Like
with any machine learning algorithm, hyperparameter tuning is important to
train a good multi-label classifier model. The task of selecting the best
hyperparameter settings for an algorithm is an optimisation problem. Very
limited work has been done on automatic hyperparameter tuning and AutoML in the
multi-label domain. This paper attempts to fill this gap by proposing a neural
network algorithm, CascadeML, to train multi-label neural network based on
cascade neural networks. This method requires minimal or no hyperparameter
tuning and also considers pairwise label associations. The cascade algorithm
grows the network architecture incrementally in a two phase process as it
learns the weights using adaptive first order gradient algorithm, therefore
omitting the requirement of preselecting the number of hidden layers, nodes and
the learning rate. The method was tested on 10 multi-label datasets and
compared with other multi-label classification algorithms. Results show that
CascadeML performs very well without hyperparameter tuning.
