.png){kind=icon}
Return
A dependency-based search strategy for feature selection
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2009.04.057.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find semi-optimal subsets. Many search strategies have been proposed in the literature, however most of them do not take into consideration relationships between features. Due to the fact that features usually have different degrees of dependency among each other, we propose in this paper a new search strategy that utilizes dependency between feature pairs to guide the search in the feature space. When compared to other well-known search strategies, the proposed method prevailed. (C) 2009 Elsevier Ltd. All rights reserved.
Keywords:
Feature selection
Search strategy
Dependency
Mutual information
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSavePrivate Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence.png)
Information
OAAI
InformationIF0
OAAIShareSaveRoots of Sobralia macrantha (Orchidaceae): structure and function of the velamen-exodermis complex.
IF0
PREAI
ShareSaveShareSave
View more