Input feature selection for classification problems

2002 ◽  
Vol 13 (1) ◽  
pp. 143-159 ◽  
Author(s):  
N. Kwak ◽  
Chong-Ho Choi
Keyword(s):  
Feature Selection ◽  
Classification Problems ◽  
Input Feature ◽  
Selection For

scholarly journals Machine learning methods in the computational biology of cancer

2014 ◽  
Vol 470 (2167) ◽  
pp. 20140081 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Feature Selection ◽  
Classification Problems ◽  
Open Problems ◽  
Machine Learning Methods ◽  
Selection For ◽  
Personalized Cancer Therapy ◽  
Personalized Cancer ◽  
Sparse Feature Selection

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

Comparing the Outcomes of Population-averaged and Personalised Input Feature Selection for Transhumeral Prosthetic Interfaces

2021 ◽  
Author(s):  
Tianshi Yu ◽  
Ricardo Garcia-Rosas ◽  
Alireza Mohammadi ◽  
Ying Tan ◽  
Peter Choong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Input Feature ◽  
Selection For
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