Preference Neural Network
This work addresses multi-label ranking with indifference preferences, a domain-specific problem, but appears incremental as it builds on existing neural network architectures with a new activation function.
The paper tackles the problem of indifference preference orders and multi-label ranking by proposing a preference neural network (PNN) with a novel smooth stairstep activation function, which outperforms five existing methods in accuracy and computational efficiency on a new dataset.
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.