Handling class overlap with variance-controlled neural networks

This paper introduces variance-controlled neural networks (VCCNs), which were developed for handling class overlap. VCNNs have the same architecture as conventional feedforward neural networks; however, their training relies on a different error function that involves variances of the network outputs. The proposed approach is benchmarked against two statistical methods, conventional feedforward neural networks, and quantum neural networks for the removal of bird-contaminated data recorded by a 1290 MHz wind profiler. The experiments indicate that VCNNs are more reliable for handling the ambiguous data involved in this application compared with the statistical methods, conventional feedforward neural networks, or quantum neural networks.