  The accuracy of a classifier, when performing Pattern recognition, is mostly
tied to the quality and representativeness of the input feature vector. Feature
Selection is a process that allows for representing information properly and
may increase the accuracy of a classifier. This process is responsible for
finding the best possible features, thus allowing us to identify to which class
a pattern belongs. Feature selection methods can be categorized as Filters,
Wrappers, and Embed. This paper presents a survey on some Filters and Wrapper
methods for handcrafted feature selection. Some discussions, with regard to the
data structure, processing time, and ability to well represent a feature
vector, are also provided in order to explicitly show how appropriate some
methods are in order to perform feature selection. Therefore, the presented
feature selection methods can be accurate and efficient if applied considering
their positives and negatives, finding which one fits best the problem's domain
may be the hardest task.
