.png){kind=icon}
Return
Feature selection for multiple binary classification problems
.png){kind=icon}
PRE
AI
DOI：10.1016/S0167-8655(99)00046-X.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The present study proposes an unsupervised method for selection of feature subsets, which retain sufficient information for classification purposes. Multiple alternative physically feasible partitions can be dealt with, using the present method. The method is based on an alternative approach to data representation, in which the axes are the data points instead of the features (a transpose projection). Under this representation, coherent features are located in the vicinity of each other, and hence can be clustered, while noisy features are pointed out and eliminated. The method bypasses the curse of dimensionality and demonstrates good results in particular in small data sets. (C) 1999 Elsevier Science B.V. All rights reserved.
Keywords:
feature selection
transpose projection
alternative partitions
clustering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more