# A Novel Generalized Artificial Neural Network for Mining Two-Class Datasets

arXiv: https://arxiv.org/abs/1910.10461 · HTML: https://mlsanity.com/paper/1910.10461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Demystifying Bitcoin Address Behavior via Graph Neural Networks](https://mlsanity.com/paper/2211.14582/) (2022)

## Abstract (arXiv)

A novel general neural network (GNN) is proposed for two-class data mining in this study. In a GNN, each attribute in the dataset is treated as a node, with each pair of nodes being connected by an arc. The reliability is of each arc, which is similar to the weight in artificial neural network and must be solved using simplified swarm optimization (SSO), is constant. After the node reliability is made the transformed value of the related attribute, the approximate reliability of each GNN instance is calculated based on the proposed intelligent Monte Carlo simulation (iMCS). This approximate GNN reliability is then compared with a given threshold to predict each instance. The proposed iMCS-SSO is used to repeat the procedure and train the GNN, such that the predicted class values match the actual class values as much as possible. To evaluate the classification performance of the proposed GNN, experiments were performed on five well-known benchmark datasets. The computational results compared favorably with those obtained using support vector machines.