**Input Variable Selection** (**IVS**), aka feature selection, is an essential step in the development of **data-driven models** and is particularly relevant in **environmental modelling**, where potential model inputs often consist of time lagged values of various, often redundant, potential input variables. IVS is often adopted for the identification of different data-driven models, such as linear regression, artificial neural networks, regression trees etc. The purpose of the IVS4EM project is to support a comprehensive framework for the testing and evaluation of IVS algorithms, through the sharing of algorithms (open source code), datasets, and evalution criteria.

A detailed description of the framework can be found in An evaluation framework for input variable selection algorithms for environmental data-driven models, by S. Galelli, G. Humphrey, H. Maier, A. Castelletti, G. Dandy and M. Gibbs (2014), ** Environmental Modelling and Software**, 62, 33-51.

To contribute your algorithm, dataset or evaluation criterion click here.

IsaHi.

That’s a nice work.

How can I reach this algorithm?

Is it possible to reach this algorithm?

Regards,

Isa

IsaI find MATLAB code. When I run it, i faced with following error in Line 62

“Undefined function or variable ‘rtenslearn_c’. ”

Regrads,

LestererartBest tests

MarioHuhHellow All I like pizza! 🙂

pizza

NilaHow can I reach this algorithm?

Is it possible to reach this algorithm?