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Physical Classification

Data Set Information:Here's the abstract from the above reference:ABSTRACT: Machine learning tools show significant......

数据结构 ? 19K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    Data Set Information:

    Here's the abstract from the above reference:

    ABSTRACT: Machine learning tools show significant promise for knowledge acquisition, particularly when human expertise is inadequate. Recently, process delays known as cylinder banding in rotogravure printing were substantially mitigated using control rules discovered by decision tree induction. Our work exemplifies a more general methodology which transforms the knowledge acquisition task from one in which rules are directly elicited from an expert, to one in which a learning system is responsible for rule generation. The primary responsibilities of the human expert are to evaluate the merits of generated rules, and to guide the acquisition and classification of data necessary for machine induction. These responsibilities require the expert to do what an expert does best: to exercise his or her expertise. This seems a more natural fit to an expert's capabilities than the requirements of traditional methodologies that experts explicitly enumerate the rules that they employ.

    Attribute Information:

    1. timestamp: numeric;19500101 - 21001231
    2. cylinder number: nominal
    3. customer: nominal;
    4. job number: nominal;
    5. grain screened: nominal; yes, no
    6. ink color: nominal;  key, type
    7. proof on ctd ink:  nominal;  yes, no  
    8. blade mfg: nominal;  benton, daetwyler, uddeholm
    9. cylinder division: nominal;  gallatin, warsaw, mattoon
    10. paper type: nominal;  uncoated, coated, super
    11. ink type: nominal;  uncoated, coated, cover
    12. direct steam: nominal; use; yes, no *
    13. solvent type: nominal;  xylol, lactol, naptha, line, other
    14. type on cylinder:  nominal;  yes, no  
    15. press type: nominal; use; 70 wood hoe, 70 motter, 70 albert, 94 motter
    16. press: nominal;  821, 802, 813, 824, 815, 816, 827, 828
    17. unit number: nominal;  1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    18. cylinder size: nominal;  catalog, spiegel, tabloid
    19. paper mill location: nominal; north us, south us, canadian, scandanavian, mid european
    20. plating tank: nominal; 1910, 1911, other
    21. proof cut: numeric;  0-100
    22. viscosity: numeric;  0-100
    23. caliper: numeric;  0-1.0
    24. ink temperature: numeric;  5-30
    25. humifity: numeric;  5-120
    26. roughness: numeric;  0-2
    27. blade pressure: numeric;  10-75
    28. varnish pct: numeric;  0-100
    29. press speed: numeric;  0-4000
    30. ink pct: numeric;  0-100
    31. solvent pct: numeric;  0-100
    32. ESA Voltage: numeric;  0-16
    33. ESA Amperage: numeric;  0-10
    34. wax: numeric ;  0-4.0
    35. hardener:  numeric; 0-3.0
    36. roller durometer:  numeric;  15-120
    37. current density:  numeric;  20-50
    38. anode space ratio:  numeric;  70-130
    39. chrome content: numeric; 80-120
    40. band type: nominal; class; band, no band *

    Relevant Papers:

    Evans, B., and Fisher, D. (1994). Overcoming process delays with decision tree induction. IEEE Expert , Vol. 9, No. 1, 60--66.
    [Web link]

    Papers That Cite This Data Set1:

    Juan J. Rodr##guez and Carlos J. Alonso. Applying Boosting to Similarity Literals for Time Series Classification. Department of Informatics University of Valladolid, Spain. 2000.  [View Context].

    Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Boosting Interval based Literals. 2000.  [View Context].

    Juan J Rodríguez Diez and Carlos Alonso González and Henrik Bostr?m. Learning First Order Logic Time Series Classifiers: Rules and Boosting. PKDD. 2000.  [View Context].

    Juan J. Rodr##guez and Carlos J. Alonso and Henrik Bostrom. Learning First Order Logic Time Series Classifiers: Rules and Boosting. Grupo de Sistemas Inteligentes, Departamento de Inform#atica Universidad de Valladolid, Spain.  [View Context].

    Charles Campbell and Nello Cristianini. Simple Learning Algorithms for Training Support Vector Machines. Dept. of Engineering Mathematics.  [View Context].

    Carlos J. Alonso Gonzalez and Juan J. Rodr and iguez Diez. Time Series Classification by Boosting Interval based Literals. Grupo de Sistemas Inteligentes Departamento de Informatica Universidad de Valladolid.  [View Context].

    Citation Request:

    Please refer to the Machine Learning Re


    Bob Evans
    RR Donnelley & Sons Co.
    Gallatin Division
    801 Steam Plant Rd
    Gallatin, Tennessee 37066-3396
    (615) 452-5170
    Donor: same




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