Handcrafted Feature Selection Techniques for Pattern Recognition: A Survey

TL;DR

This survey reviews handcrafted feature selection techniques for pattern recognition, discussing their categories, advantages, disadvantages, and suitability based on data structure and processing time.

Contribution

It provides a comprehensive overview of filter and wrapper feature selection methods, analyzing their effectiveness and applicability in pattern recognition tasks.

Findings

Filter and wrapper methods vary in accuracy and efficiency.

Method suitability depends on data structure and computational constraints.

Choosing the right feature selection method is crucial for optimal pattern recognition performance.

Abstract

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.