A multi-layer perceptron neural network for varied conditional attributes in tabular dispersed data.

The paper introduces a novel approach for constructing a global model utilizing multilayer perceptron (MLP) neural networks and dispersed data sources. These dispersed data are independently gathered in various local tables, each potentially containing different objects and attributes, albeit with some shared elements (objects and attributes). Our approach involves the development of local models based on these local tables imputed with some artificial objects.

Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data.

This study concerns dispersed data stored in independent local tables with different sets of attributes. The paper proposes a new method for training a single neural network-a multilayer perceptron based on dispersed data. The idea is to train local models that have identical structures based on local tables; however, due to different sets of conditional attributes present in local tables, it is necessary to generate some artificial objects to train local models.

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources.

The article concerns the problem of classification based on independent data sets-local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables.