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Numerous approaches for feature point detection exist in the literature.
Besides, two different combining approaches for feature selection are studied.
The employed approaches for feature extraction and selection are discussed further in Section 3.3.
Three different approaches for feature selection for QSAR problems based on evolutionary algorithms (EA) are addressed in this chapter.
To validate the method, we tested it with the feature algorithm known as speeded up robust features (SURF), one of the most efficient approaches for feature extraction.
We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation.
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This work presents our FeLLaCaM approach for Feature Location.
Note that there has been no systematic approach for feature detection in outdoor environments.
The chapter also discusses the filter approach and the Wrapper approach for feature selection.
Furthermore, the usefulness of the approach for feature selection is also analyzed.
Specifically, the authors developed a MapReduce implementation based on the evolutionary approach for Feature Weighting proposed in [58].
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