What is Group Method of Data Handling mean? Group method of data handling (GMDH) is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models.
GMDH is used in such fields as data mining, knowledge discovery, prediction, complex systems modeling, optimization and pattern recognition. GMDH algorithms are characterized by inductive procedure that performs sorting-out of gradually complicated polynomial models and selecting the best solution by means of the external criterion.
A GMDH model with multiple inputs and one output is a subset of components of the base function (1):
Y ( x 1 , … , x n ) = a 0 + ∑ i = 1 m a i f i {\displaystyle Y(x_{1},\dots ,x_{n})=a_{0}+\sum \limits _{i=1}^{m}a_{i}f_{i}}
where fi are elementary functions dependent on different sets of inputs, ai are coefficients and m is the number of the base function components.
In order to find the best solution GMDH algorithms consider various component subsets of the base function (1) called partial models. Coefficients of these models are estimated by the least squares method. GMDH algorithms gradually increase the number of partial model components and find a model structure with optimal complexity indicated by the minimum value of an external criterion. This process is called self-organization of models.
As the first base function used in GMDH, was the gradually complicated Kolmogorov–Gabor polynomial (2):
Y ( x 1 , … , x n ) = a 0 + ∑ i = 1 n a i x i + ∑ i = 1
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Posted on 02 Jan 2025, this text provides information on Networking related to Miscellaneous in Networking. Please note that while accuracy is prioritized, the data presented might not be entirely correct or up-to-date. This information is offered for general knowledge and informational purposes only, and should not be considered as a substitute for professional advice.