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

ABSTRACT: 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. Subsequently, local models are aggregated using weighted techniques. To complete, the global model is retrained using some global objects. In this study, the proposed method is compared with two existing approaches from the literature-homogeneous and heterogeneous multi-model classifiers. The analysis reveals that the proposed appro

SUBMITTER: Przybyla-Kasperek M 

PROVIDER: S-EPMC11611123 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature