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Group Method of Data Handling* was applied in a great variety of areas for data mining and knowledge discovery, forecasting and systems modeling, optimization and pattern recognition. Inductive GMDH algorithms give possibility to find automatically interrelations in data, to select an optimal structure of model or network and to increase the accuracy of existing algorithms.

This original self-organizing approach is substantially different from deductive methods used commonly for modeling. It has inductive nature - it finds the best solution by sorting-out of possible variants.

Criterion Characteristic By sorting of different solutions GMDH networks aims to minimize the influence of the author on the results of modeling. Computer itself finds the structure of the optimal model or laws that act in a system.

Group Method of Data Handling is a set of several algorithms for different problems solution. It consists of parametric, clusterization, analogues complexing, rebinarization and probability algorithms. This inductive approach is based on sorting-out of gradually complicated models and selection of the optimal solution by minimum of external criterion characteristic. Not only polynomials but also non-linear, probabilistic functions or clusterizations are used as basic models.

GMDH approach can be useful because:

  • Optimal complexity of the model structure is found, adequate to the level of noise in data sample. For real problems, with noised or short data, a simplified optimal models are more accurate.
  • The number of layers and neurons in hidden layers, model structure and other optimal neuran networks parameters are determined automatically.
  • It guarantees that the most accurate or unbiased models will be found - method doesn't miss the best solution during sorting of all variants (in the given class of functions).
  • As input variables are used any non-linear functions or features, which can influence the output variable.
  • It automatically finds interpretable relationships in data and selects effective input variables.
  • GMDH sorting algorithms are rather simple for programming.
  • Twice-multilayered neural nets can be used to increase the accuracy of another modelling algorithms.
  • Method get information directly from data sample and minimizes influence of apriori author assumptions about results of modeling.
  • Approach gives possibility to find unbiased physical model of object (law or clusterization) - one and the same for future samples.

It was implemented in the many commercial software tools.

A.G.IvakhnenkoThis year we celebrate the 100th anniversary of Acad. Olexiy Gryhorovych Ivakhnenko (30.03.1913-16.10.2007), founder of the scientific school of inductive modelling.

* GMDH is known also as Polynomial Neural Networks, Abductive and Statistical Learning Networks
GMDH News
Conference
  • The 4th International Conference on Inductive Modeling (ICIM'2013) was held in Kyiv, Ukraine

    Publications
  • Proceedings of the V International Workshop on Inductive Modeling were added

  • The book 'Complex Systems Modelling by Experimental Data' was added to library

    Software
  • Two data modeling tools, KnowledegeMiner (yX) for Excel and KnowledgeMiner were released for OS X

  • GMDH Shell is the advanced but easy to use tool for data mining

  • GMDH PNN algorithm is available for on-line computation on the first and second sites