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）来解决带有新激励函数的不同偏好顺序问题。PNN也解决了多标签排序问题，其中标签可能具有不同偏好顺序或子组被等级相同。PNN遵循多层前馈体系结构，具有全连接神经元。每个神经元包含一种基于偏好顺序数量的新型平滑阶梯形激活函数。PNN输入表示数据特征，输出神经元表示标签索引。所提出的PNN使用包含重复标签值的新偏好挖掘数据集进行评估，这是以前没有经历过的。在准确结果和高计算效率方面，PNN优于以前提出的五种严格标签排序方法。