机器学习_Lymphography Data Set(淋巴系造影术数据集)

1、Lymphography Data Set(淋巴系造影术数据集数据摘要:This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access中文关键词:淋巴系造影术,多变量,分类,UCI,英文关键词:Lymphography,Multivariate,Classification,UCI,数据格式:TEXT数据用途:This data set is used for classificat

2、ion.数据详细介绍:Lymphography Data SetAbstract: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access Source:Donors:1. Igor Kononenko,University E.KardeljFaculty for electrical engineeringTrzaska 2561000 Ljubljana (tel.:

3、(38(+61 265-1612. Bojan CestnikJozef Stefan InstituteJamova 3961000 LjubljanaYugoslavia (tel.: (38(+61 214-399 ext.287Data Set Information:This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also breast-cancer and prim

4、ary-tumor.Attribute Information:- NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below.1. class: normal find, metastases, malign lymph, fibrosis2. lymphatics: normal, arche

5、d, deformed, displaced3. block of affere: no, yes4. bl. of lymph. c: no, yes5. bl. of lymph. s: no, yes6. by pass: no, yes7. extravasates: no, yes8. regeneration of: no, yes9. early uptake in: no, yes10. lym.nodes dimin: 0-311. lym.nodes enlar: 1-412. changes in lym.: bean, oval, round13. defect in

6、node: no, lacunar, lac. marginal, lac. central14. changes in node: no, lacunar, lac. margin, lac. central15. changes in stru: no, grainy, drop-like, coarse, diluted, reticular, stripped, faint,16. special forms: no, chalices, vesicles17. dislocation of: no, yes18. exclusion of no: no, yes19. no. of

7、nodes in: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, >=70Relevant Papers:Cestnik,G., Konenenko,I, & Bratko,I. (1987. Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds. Progress in Machine Learning, 31-45, Sigma Press.Web LinkClark,P. &

8、 Niblett,T. (1987. Induction in Noisy Domains. In I.Bratko & N.Lavrac (Eds. Progress in Machine Learning, 11-30, Sigma Press.Web LinkMichalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986. The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In Proceedings of