Handcrafted Feature Selection Techniques for Pattern Recognition: A Survey
It provides an incremental overview of feature selection techniques for researchers and practitioners in pattern recognition, without introducing new methods.
This survey reviews handcrafted feature selection methods, including Filters and Wrappers, for pattern recognition, discussing their accuracy, efficiency, and suitability based on data structure and processing time.
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.