By Topic

Information-theoretic feature selection for a neural behavioral model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
B. Chambless ; Unicru Inc., Beaverton, OR, USA ; D. Scarborough

Employers of hourly workers typically experience high employee turnover. Due to costs associated with: training, hiring and termination, the overhead from this high turnover rate is substantial. It is therefore desirable to construct employee selection procedures and analytic models to estimate the likely tenure of applicants for employment prior to a hiring decision. A critical component in the success of this effort to create a neural network model to estimate tenure was the application of information-theoretic feature selection. The benefits of this technique are demonstrated by comparison with results obtained using no feature selection and alternate methods of feature selection

Published in:

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:2 )

Date of Conference:

2001