A Review of Feature Selection

محورهای موضوعی : یادگیری ماشینی

Jafar Abdollahi ¹ , Babak Nouri-Moghaddam ² , Naser Mikaeilvand ³ , Sajjad Jahanbakhsh Gudakahriz ⁴ , Ailin Khosravani ⁵ , Abbas Mirzaei ⁶

1 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
3 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Computer Engineering, Germi Branch, Islamic Azad University, Germi, Iran
5 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
6 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran

تاریخ دریافت : 1403/09/08 تاریخ پذیرش : 1403/12/23 تاریخ انتشار : 1404/01/16

کلید واژه: Data Mining, Medical Applications, Dimension Reduction, Feature Selection,

چکیده مقاله :

Feature selection is a preprocessing technique that identifies the salient features of a given scenario. It has been used in the past for a wide range of problems, including intrusion detection systems, financial problems, and the analysis of biological data. Feature selection has been especially useful in medical applications, where it may help identify the underlying reasons for an illness in addition to reducing dimensionality. We provide some basic concepts of medical applications and the necessary background information on feature selection. We review the most recent feature selection methods developed for and applied to medical problems, covering a broad spectrum of applications including medical imaging, DNA microarray data analysis, and biomedical signal processing. A case study of two medical applications utilizing actual patient data is used to demonstrate the usefulness of applying feature selection techniques to medical challenges and to highlight how these methods function in practical scenarios.

چکیده انگلیسی:

Feature selection is a preprocessing technique that identifies the salient features of a given scenario. It has been used in the past for a wide range of problems, including intrusion detection systems, financial problems, and the analysis of biological data. Feature selection has been especially useful in medical applications, where it may help identify the underlying reasons for an illness in addition to reducing dimensionality. We provide some basic concepts of medical applications and the necessary background information on feature selection. We review the most recent feature selection methods developed for and applied to medical problems, covering a broad spectrum of applications including medical imaging, DNA microarray data analysis, and biomedical signal processing. A case study of two medical applications utilizing actual patient data is used to demonstrate the usefulness of applying feature selection techniques to medical challenges and to highlight how these methods function in practical scenarios.

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