Feature Selection

By Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, Huan Liu. ACM Computing Surveys, 2016

1,161 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ad3c82ada3ff848bb36bade1d90820c2e465b2d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yingchun Xia
2. Xianzhang Shi
3. Mian Dai
4. Lichuan Gu
5. Han Yu
6. Xiaohui Yuan
7. Juanjuan Feng
8. Shanqing Jiang

In Computer Vision and Pattern Recognition

1. Mian Dai
2. Lichuan Gu
3. Han Yu
4. Xiaohui Yuan
5. Shanqing Jiang
6. Yuyang Zhou
7. Guang Cheng
8. Siwei Feng

Citations in the corpus

• Feature selection based on artificial bee colony and gradient boosting decision tree (2019, background): “Its advantages have been demonstrated in various data mining and machine learning applications [9,10].”
• Multi-Participant Multi-Class Vertical Federated Learning (2020, methodology): “Feature Selection Feature selection is a set of frequently used dimensionality reduction approaches for selecting a subset of useful features from a dataset for a given learning task [Li et al., 2017].”
• Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, background): “rocessing Data preprocessing is the most time consuming and essential step in data mining. Realistic data typically comes from heterogeneous platformsandcanbenoisy,redundant,incomplete,andinconsistent[40]. Thus, it is important to transform raw data into a format suitable for analysis and knowledge ”