.png){kind=icon}
Return
Comparative analysis on margin based feature selection algorithms
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-013-0164-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Feature evaluation and selection is an important preprocessing step in classification and regression learning. As large quantity of irrelevant information is gathered, selecting the most informative features may help users to understand the task, and enhance the performance of the models. Margin has been widely accepted and used in evaluating feature quality these years. A collection of feature selection algorithms were developed using margin based loss functions and various search strategies. However, there is no comparative research conducted to study the effectiveness of these algorithms. In this work, we compare 14 margin based feature selections from the viewpoints of reduction capability, classification performance of reduced data and robustness, where four margin based loss functions and three search strategies are considered. Moreover, we also compare these techniques with two well-known margin based feature selection algorithms ReliefF and Simba. The derived conclusions give some guidelines for selecting features in practical applications.
Keywords:
Classification learning
Feature selection
Margin
Search strategy
Robustness
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
3.2K
Citations:
5.6K
Organization
Cited Papers
ShareSaveShareSaveSignificance of elevated urinary N-acetyl-β-D-glucosaminidase activity in chronic cadmium poisoning
PREAI
ShareSaveShareSaveFeature selection based on loss-margin of nearest neighbor classification.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSave
View more