The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 28-865
Full Name: Hsin-Yun Chang
Position: Assistant Professor
Age: ON
Sex: Female
Address: 110 Hsueh-Fu Road, Tou-Fen, Miao-Li 305, Taiwan, R.O.C.
Country: TAIWAN
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E-mail address: ran_hsin@ms.chinmin.edu.tw
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Title of the Paper: Employee Turnover: A Novel Prediction Solution with Effective Feature Selection
Authors as they appear in the Paper: Hsin-Yun Chang
Email addresses of all the authors: ran_hsin@ms.chinmin.edu.tw
Number of paper pages: 10
Abstract: This study proposed to address a new method that could select subsets more efficiently. In addition, the reasons why employers voluntarily turnover were also investigated in order to increase the classification accuracy and to help managers to prevent employers¡¦ turnover. The mixed feature subset selection used in this study combined Taguchi method and Nearest Neighbor Classification Rules to select feature subset and analyze the factors to find the best predictor of employer turnover. All the samples used in this study were from industry A, in which the employers left their job during 1st of February, 2001 to 31st of December, 2007, compared with those incumbents. The results showed that through the mixed feature subset selection method, total 18 factors were found that are important to the employers. In addition, the accuracy of correct selection was 87.85% which was higher than before using this feature subset selection method (80.93%). The new feature subs!
et selection method addressed in this study does not only provide industries to understand the reasons of employers¡¦ turnover, but also could be a long-term classification prediction for industries.
Keywords: Voluntary turnover; Feature subset selection; Taguchi methods; Nearest neighbor classification rules; Training pattern
EXTENSION of the file: .pdf
Special (Invited) Session: Employee Turnover: A Novel Prediction Solution with Effective Feature Selection
Organizer of the Session: 610206
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