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.

翻译：本文提出了一种偏好神经网络（PNN），通过引入新型激活函数来解决无差异偏好排序问题。PNN还解决了多标签排序问题，其中标签可能存在无差异偏好顺序，或子组被同等排序。PNN采用全连接神经元的多层前馈架构，每个神经元包含一种基于偏好顺序数量的新型平滑阶梯激活函数。PNN的输入表示数据特征，输出神经元代表标签索引。本文利用包含重复标签值（此前未被实验验证过）的新偏好挖掘数据集对所提出的PNN进行了评估。在严格标签排序的精确性方面，PNN在保持高计算效率的同时，优于五种先前提出的方法。