Semantic Scholar Open Access 2018 1752 sitasi

Feature selection in machine learning: A new perspective

Jie Cai Jiawei Luo Shulin Wang Sheng Yang

Lihat Sumber DOI

Abstrak

Abstract High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and facilitate a better understanding for the learning model or data. In this study, we discuss several frequently-used evaluation measures for feature selection, and then survey supervised, unsupervised, and semi-supervised feature selection methods, which are widely applied in machine learning problems, such as classification and clustering. Lastly, future challenges about feature selection are discussed.

Topik & Kata Kunci

Computer Science

Penulis (4)

Jie Cai

Jiawei Luo

Shulin Wang

Sheng Yang

Format Sitasi

APA MLA BibTeX

Cai, J., Luo, J., Wang, S., Yang, S. (2018). Feature selection in machine learning: A new perspective. https://doi.org/10.1016/j.neucom.2017.11.077

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.neucom.2017.11.077

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1752×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.neucom.2017.11.077
Akses: Open Access ✓