Ecological data sets are often high dimensional: data sets derived from satellite imagery may contain hundreds of intercorrelated spectral bands. It is desirable to train from a reduced set of attributes, to reduce both the size of the model-fitting search space, and the risk of over-fitting the data.
There are two common approaches: filters and wrappers. Filter methods pre-analyze the data to select a suitable subset of the available features - for example, principal component analysis may be used to generate and select a small number ofhighly influential features for training. However, the model-learning phase may uncover higher-order interactions which are invisible to a filter approach; these are better handled by wrapper approaches, which 'wrap' an optimization algorithm around the underlying model-learning algorithm, repeatedly training from different feature sets until a good feature set is found. Evolutionary algorithms are frequently used as the wrapper.
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