Abstract
Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.
Highlights
In data mining and machine learning, real-world problems often involve a large number of features
Peng et al [32] proposed the minimum Redundancy Maximum Relevance method based on mutual information, where the proposed measures have been introduced to Evolutionary computation (EC) for feature selection due to their powerful search abilities [33], [34]
This paper provided a comprehensive survey of EC techniques in solving feature selection problems, which covered all the commonly used EC algorithms and focused on the key factors, such as representation, search mechanisms, and the performance measures as well as the applications
Summary
In data mining and machine learning, real-world problems often involve a large number of features. Based on the evaluation criteria, feature selection algorithms are generally classified into two categories: filter approaches and wrapper approaches [1], [2]. Their main difference is that wrapper approaches include a classification/learning algorithm in the feature subset evaluation step. The methods integrating feature selection and classifier learning into a single process are called embedded approaches. To simplify the structure of the paper, we follow the convention of classifying feature selection algorithms into wrappers and filters only [1], [2], [21] with embedded algorithms belonging to the wrapper category
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
