Feature Selection: A Data Perspective
Open accessAbstract
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.
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There are needs of designing tools for ML applications, such as tools for non-expert ML designers, visualization tools for understanding the relationships between data and the behavior of algorithms, tools for ensuring interoperability with other tools, and domain-specific language (DSL) support.
“-Tools: Needs of designing tools for ML applications, such as tools for non-expert ML designers, visualization tools for understanding the relationships between data and the behavior of algorithms, tools for ensuring interoperability with other tools, and domain-specific language (DSL) support [16,25,42,66].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [16]
Challenges on the software structure of ML applications include complex software modules of ML algorithms, anti-patterns in ML applications, and various design issues in ML models (model selection, customization and reuse).
“-Software Structure: Challenges on the software structure of ML applications.This category includes the complex software modules of ML algorithms [14], anti-patterns in ML applications [17], and various design issues in ML models (model selection, customization and reuse) [16,34,58,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [16]
In a real-world ML application project for a company, the development, deployment and operation may be severely affected by the effort of complying with the privacy policy of the organization and the legal framework, imposing challenges from both technical and management perspectives.
“-Corporate Compliance: In a real-world ML application project for a company, the development, deployment and operation may be severely affected by the effort of complying with the privacy policy of the organization and the legal framework.These demands impose challenges from both technical and management perspectives [16,34,60,107].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [16]
Developing the skill set for ML applications is the major challenge related to the software engineering professional practice knowledge area.
“Developing the skill set for ML applications is the major challenge related to this KA [15,16,27,32,39,40,59].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [16]
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- Authors
- Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H.
- Affiliations
- Arizona State University (US)
- Arizona State University, Tempe, AZ
- Michigan State University (US)
- Venue
- ACM Computing Surveys
- Published
- 2017-12-06
- Volume
- 50
- Issue
- 6
- Pages
- 1-45
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4289236186
- Cited by
- 2,442
- References
- 116
- Citations by year
- 2026: 256 · 2025: 472 · 2024: 442 · 2023: 372 · 2022: 305 · 2021: 239 · 2020: 175 · 2019: 130 · 2018: 49 · 2017: 2
- FWCI
- 67.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Computing Surveys (publishedVersion)
- Funders
- NSF (1217466 and 1614576)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- feature selection, high-dimensional data, big data, similarity-based methods, sparse learning, statistical feature selection, structured data, data heterogeneity, data streams, feature selection algorithms
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