.png){kind=icon}
Return
Fuzzy kernel feature selection with multi-objective differential evolution algorithm
.png){kind=icon}
PRE
AI
DOI：10.1080/09540091.2019.1639624.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.
Keywords:
Kernel space
fuzzy information theory
multi-objective optimisation
differential evolution
feature selection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
854
Citations:
1.5K
Organization
Cited Papers
.png){kind=icon}
ShareSaveA novel hybrid genetic algorithm with granular information for feature selection and optimization
PREAI
ShareSaveShareSave
ShareSave
View more