Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries

Flexible Services and Manufacturing Journal

Volumn 23, Issue 3, 2011, Pages 263-289

  Chen, Li Fei ^a   Chien, Chen Fu ^b  

^a FU JEN CATHOLIC UNIVERSITY   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Data mining; Decision tree; Human capital; Manufacturing intelligence; Rough set theory; Support vector machine

Indexed keywords

CLASS PREDICTION; COMPETITIVE ADVANTAGE; CRITICAL RESOURCES; DECISION QUALITY; EMPIRICAL STUDIES; HIGH-TECH INDUSTRIES; HUMAN CAPITAL; HUMAN CAPITALS; JOB CHARACTERISTICS; KNOWLEDGE WORKERS; PRODUCTION DATA; ROUGH SET; RULE GENERATION; SEMICONDUCTOR COMPANIES; SEMICONDUCTOR MANUFACTURING; SUPPORT VECTOR;

COMPETITION; DATA MINING; DECISION THEORY; DECISION TREES; ENTERPRISE RESOURCE MANAGEMENT; FORESTRY; INDUSTRY; INFORMATION MANAGEMENT; MANUFACTURE; PERSONNEL SELECTION; SEMICONDUCTOR DEVICE MANUFACTURE; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

ROUGH SET THEORY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPETITION; DECISION MAKING; FORESTRY; MANUFACTURE;

EID: 81855175379     PISSN: 19366582     EISSN: 19366590     Source Type: Journal    
DOI: 10.1007/s10696-010-9068-x     Document Type: Article

References (45)

1. Appleyard MM, Brown C (2001) Employment practices and semiconductor manufacturing performance. Ind Relat 40(3):436-471 (Pubitemid 32745639)
2. Baesens B, Mues C, Martens D, Vanthienen J (2009) 50 years of data mining and OR: upcoming trends and challenges. J Oper Res Soc 60:S16-S23
3. Berry MJ, Linoff G (1997) Data mining techniques: for marketing, sales, and customer support. Wiley, New York
4. Breiman L, Friedman JH, Olshen RA, Stone PJ (1984) Classification and regression trees. Wadsworth International Group, California
5. Cao LJ, Tay F E H (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506-1518
6. Charest M, Delisle S, Cervantes O, Shen YF (2008) Bridging the gap between data mining and decision support: a case-based reasoning and ontology approach. Intell Data Anal 12(2):211-236
7. Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866-883 (Pubitemid 126774144)
8. Chien CF, Chen LF (2007) Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing. IEEE Trans Semicond Manuf 20(4):528-541
9. Chien CF, Wang I, Chen LF (2005) Using data mining to improve the quality of human resource management of operators in semiconductor manufactures. J Qual 12(1):9-28
10. Chien CF, Wang W, Cheng J (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192-198 (Pubitemid 44959928)
11. Chien CF, Chen Y, Peng J (2010) Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. Int J Prod Econ 128(2):496-509
12. Cho V, Ngai E (2003) Data mining for selection of insurance sales agents. Expert Syst 20(3):123-132
13. Choo YH, Abu Bakar A, Hamdan AR (2008) The fitness-rough: a new attribute reduction method based on statistical and rough set theory. Intell Data Anal 12(1):73-87
14. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(1):273-297
15. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge
16. De Souza JT, Matwin S, Japkowicz N (2006) Parallelizing feature selection. Algorithmica 45(3):433-456 (Pubitemid 43876798)
17. Fayyad U, Piatesky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39:27-34 (Pubitemid 126428076)
18. He J, Hu H, Harrison R, Tai PC, Pan Y (2006) Rule generation for protein secondary structure prediction with support vector machines and decision tree. IEEE Trans Nanobiosci 5(1):46-53
19. Hooper RS, Galvin TP, Kilmer RA, Liebowitz J (1998) Use of an expert system in a personnel selection process. Expert Syst Appl 14(4):425-432 (Pubitemid 128431037)
20. Hough LM, Oswald FL (2000) Personnel selection: looking toward the future: remembering the past. Annu Rev Psychol 51:631-664
21. Hsu S, Chien CF (2007) Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. Int J Prod Econ 107(1):88-103 (Pubitemid 46241857)
22. Huang LC, Kuo RJ, Huang HJ (2002) A neural network modeling for human resource talent selection. Int J Hum Res Dev Manag 1(2/3/4):206-219
23. Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119-127
24. Kovach KA, Cathcart CE (1999) Human resource information systems (HRIS): providing business with rapid data access, information exchange and strategic advantage. Public Pers Manag 28(2):275-282
25. Krishnan R, Sivakumar G, Bhattacharya P (1999) Extracting decision trees from trained neural networks. Pattern Recognit 32(12):1999-2009
26. Kuo C, Chien CF, Chen C (2010) Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE transactions on automation science and engineering (Digital Object Identifier 10.1109/TASE.2010.2040999)
27. Li P, Wang Z (2004) Mining classification rules using rough sets and neural networks. Eur J Oper Res 157(5-6):439-448
28. Lievens F, Van Dam K, Anderson N (2002) Recent trends and challenges in personnel selection. Pers Rev 31:580-601
29. Mak B, Munakata T (2002) Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3. Eur J Oper Res 136:212-229 (Pubitemid 33018813)
30. Mohanty RP, Deshmukh SG (1997) Evolution of a decision support system for human planning in a petroleum company. Int J Prod Econ 51(3):251-261 (Pubitemid 127403532)
31. Ntuen CA, Chestnut JA (1995) An expert-system for selecting manufacturing workers for training. Expert Syst Appl 9(3):309-332
32. Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11:341-356
33. Pawlak Z (1997) Rough sets approach to knowledge-based decision support. Eur J Oper Res 99:48-57 (Pubitemid 127443690)
34. Pyle D (1999) Data preparation for data mining. Morgan Kaufrnann, San Francisco, CA
35. Quinlan JR (1986) Induction of decision tree. Mach Learn 1(1):81-106
36. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo, CA
37. Robertson IT, Smith M (2001) Personnel selection. J Occup Org Psychol 74(4):441-472
38. Shih PC, Liu CJ (2006) Face detection using discriminating feature analysis and support vector machine. Pattern Recognit 39(2):260-276
39. Su CT, Yih YW, Chen LS (2006) Knowledge acquisition through information granulation for imbalanced data. Expert Syst Appl 31(3):531-541
40. Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recognit Lett 24(6):833-849 (Pubitemid 35391801)
41. Tavana M, Kennedy DT, Joglekar P (1996) A group decision support framework for consensus ranking of technical manager candidates. Omega-Int J Manag Sci 24(5):523-538 (Pubitemid 126166532)
42. Wang S, Dash M, Chia LT (2006) Efficient data reduction in multimedia data. Appl Intell 25:359-374 (Pubitemid 44701187)
43. *rses
44. Zhan YM, Zeng XY, Sun JC (2005) Rough set-based feature selection method. Prog Nat Sci 15(3):280-284
45. Zhong N, Dong J, Ohsuga S (2001) Using rough sets with heuristics for feature selection. J Intell Inf Syst 16:199-214 (Pubitemid 32886812)