# Sequential three-way decisions with a single hidden layer feedforward neural network

arXiv: https://arxiv.org/abs/2303.07589 · HTML: https://mlsanity.com/paper/2303.07589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 58 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019)
- [AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices](https://mlsanity.com/paper/2102.01875/) (2021)
- [Provable Convergence of Nesterov's Accelerated Gradient Method for Over-Parameterized Neural Networks](https://mlsanity.com/paper/2107.01832/) (2021)
- [A Temporally-Aware Interpolation Network for Video Frame Inpainting](https://mlsanity.com/paper/1803.07218/) (2018)
- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [DBC-Forest: Deep forest with binning confidence screening](https://mlsanity.com/paper/2112.13182/) (2021)

## Abstract (arXiv)

The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed threshold parameters. Inspired by the sequential three-way decisions (STWD), this paper proposes STWD with an SFNN (STWD-SFNN) to enhance the performance of networks on structured datasets. STWD-SFNN adopts multi-granularity levels to dynamically learn the number of hidden layer nodes from coarse to fine, and set the sequential threshold parameters. Specifically, at the coarse granular level, STWD-SFNN handles easy-to-classify instances by applying strict threshold conditions, and with the increasing number of hidden layer nodes at the fine granular level, STWD-SFNN focuses more on disposing of the difficult-to-classify instances by applying loose threshold conditions, thereby realizing the classification of instances. Moreover, STWD-SFNN considers and reports the process cost produced from each granular level. The experimental results verify that STWD-SFNN has a more compact network on structured datasets than other SFNN models, and has better generalization performance than the competitive models. All models and datasets can be downloaded from https://github.com/wuc567/Machine-learning/tree/main/STWD-SFNN.