  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.

Neural and Evolutionary Computing;Signal Processing