Classification and integration of multitype data

Neural network approaches and statistical classification methods based on consensus from several data sources are considered with respect to classification and integration of multitype data. The consensus theoretic methods need weighting mechanisms to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. A nonlinear method which utilizes a neural network is used and trained on a feature reduced input set. This nonlinear method gives excellent results in experiments along with other neural network models