Preference Neural Network

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.

Subgroup Preference Neural Network

“…These fluctuations are not related to the gradient error in ranking, but it is the average ranking between two subgroups as each subgroup tends to increase the ranking, it updates its weights which reflect on the shared weights, which may reduce the convergence of the second group. The fluctuation is shown in the video link of convergence of two groups using toy dataset [ 43 ]. The convergence fluctuations are not noticed when we use three subgroups together, i.e., the iris-wine-stock dataset using the same hyper-parameters of two subgroups SGPNN .…”

“…The preference neural network ( PNN ) is a simple fully connected network with a single hidden layer which provides desirable ranking performance due to the SS activation function [ 39 ]. We performed experiments on 12 benchmark label ranking datasets [ 26 ] which show that increasing the number of hidden layers does not improve the performance, but rather it has adverse effects.…”

“…The learning stops after 20,000 epochs or Spearman’s reaches 1. A video demo that shows the ranking learning process using simple toy data are available at [ 43 ].…”

“… An example of Two subgroups architecture of SGPNN to rank conjoint data from two subgroups data, each group has 4 and 3 labels respectively, where , , . A video demo of 2-subgroup architecture is available in [ 43 ]. …”