  This paper proposes a preference neural network (PNN) to address the problem
of indifference preferences orders with new activation function. PNN also
solves the Multi-label ranking problem, where labels may have indifference
preference orders or subgroups are equally ranked. PNN follows a multi-layer
feedforward architecture with fully connected neurons. Each neuron contains a
novel smooth stairstep activation function based on the number of preference
orders. PNN inputs represent data features and output neurons represent label
indexes. The proposed PNN is evaluated using new preference mining dataset that
contains repeated label values which have not experimented before. PNN
outperforms five previously proposed methods for strict label ranking in terms
of accurate results with high computational efficiency.
