# Preference Neural Network

## TL;DR

This paper introduces a Preference Neural Network (PNN) with a novel activation function to effectively handle indifference preferences and multi-label ranking, demonstrating superior accuracy and efficiency on a new dataset.

## Contribution

The paper presents a new neural network architecture with a smooth stairstep activation function designed for preference ranking with indifference and subgroup considerations.

## Key findings

- PNN outperforms five existing methods in accuracy.
- PNN achieves high computational efficiency.
- PNN effectively handles indifference preferences in ranking.

## Abstract

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.