Preprint Article Version 1 This version is not peer-reviewed

Preference Neural Network

Version 1 : Received: 7 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (11:50:05 CEST)

How to cite: Elgharabawy, A.; Parsad, M.; Lin, C. Preference Neural Network. Preprints 2019, 2019040091 (doi: 10.20944/preprints201904.0091.v1). Elgharabawy, A.; Parsad, M.; Lin, C. Preference Neural Network. Preprints 2019, 2019040091 (doi: 10.20944/preprints201904.0091.v1).

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.

Subject Areas

preference learning; neural network; multi-label ranking

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