Group Method of Data Handling: A New Method for Stock Identification

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Abstract
The group method of data handling (GMDH) is described and applied to the problem of fish stock separation (classification). The method, which increases model complexity in a stepwise fashion, finds an empirical polynomial model of a complex system that satisfies any of several optimality criteria. By basing the model's structure largely on the data, rather than a priori assumptions, GMDH can reduce the number of assumptions that must be made about the system under study. I analyzed data from two previously published data sets with GMDH to arrive at stock identification models for American shad Alosa sapidissima and striped bass Morone saxatilis. Each model was verified with data not used in model construction. The GMDH-based models were as good as conventional discriminant models at classifying the verification samples of fish. In addition, GMDH was able to identify excellent models that used fewer characteristics (features) of the samples.

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