Version 1
: Received: 7 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (11:50:05 CEST)
Version 2
: Received: 5 June 2020 / Approved: 5 June 2020 / Online: 5 June 2020 (04:37:39 CEST)
Version 3
: Received: 5 June 2020 / Approved: 7 June 2020 / Online: 7 June 2020 (17:44:06 CEST)
Version 4
: Received: 23 December 2021 / Approved: 24 December 2021 / Online: 24 December 2021 (16:08:06 CET)
Version 5
: Received: 18 April 2023 / Approved: 19 April 2023 / Online: 19 April 2023 (07:43:17 CEST)
Elgharabawy, A.; Prasad, M.; Lin, C.-T. Preference Neural Network. IEEE Transactions on Emerging Topics in Computational Intelligence 2023, 1–15, doi:10.1109/tetci.2023.3268707.
Elgharabawy, A.; Prasad, M.; Lin, C.-T. Preference Neural Network. IEEE Transactions on Emerging Topics in Computational Intelligence 2023, 1–15, doi:10.1109/tetci.2023.3268707.
Elgharabawy, A.; Prasad, M.; Lin, C.-T. Preference Neural Network. IEEE Transactions on Emerging Topics in Computational Intelligence 2023, 1–15, doi:10.1109/tetci.2023.3268707.
Elgharabawy, A.; Prasad, M.; Lin, C.-T. Preference Neural Network. IEEE Transactions on Emerging Topics in Computational Intelligence 2023, 1–15, doi:10.1109/tetci.2023.3268707.
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
