HDF  Pu` TREEHEAPX datadata_descr8 0name 'breastTumor' (mldata0 (comment !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Tumor-size treated as the class attribute. As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems. Singapore: Springer-Verlag. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database. 1. Title: Breast cancer data (Michalski has used this) 2. Sources: -- Matjaz Zwitter & Milan Soklic (physicians) Institute of Oncology University Medical Center Ljubljana, Yugoslavia -- Donors: Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 11 July 1988 3. Past Usage: (Several: here are some) -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann. -- accuracy range: 66%-72% -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press. -- 8 test results given: 65%-72% accuracy range -- Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI. -- 4 systems tested: accuracy range was 68%-73.5% -- 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. -- Assistant-86: 78% accuracy 4. Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.) This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal. 5. Number of Instances: 286 6. Number of Attributes: 9 + the class attribute 7. Attribute Information: 1. Class: no-recurrence-events, recurrence-events 2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99. 3. menopause: lt40, ge40, premeno. 4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59. 5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39. 6. node-caps: yes, no. 7. deg-malig: 1, 2, 3. 8. breast: left, right. 9. breast-quad: left-up, left-low, right-up, right-low, central. 10. irradiat: yes, no. 8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances with missing values: 6. 8 9. 1. 9. Class Distribution: 1. no-recurrence-events: 201 instances 2. recurrence-events: 85 instances TREEhBHHEAP`O`D QSNOD80LSNOD`P80LxPCXPNp@h?********** *********************** * * * ******************************************************************************************************************** * * * * ******************* *!*"*#*$*%*&*'*(*)***+*,*-*.*/*0*1*2*3*4*5*6*7*8*9*:*;*<*=*>*?*@*A*B*C*D*E*F*G*H*I*J*K*L*M*N*O*P*Q*R*S*T*U*V*W*X*Y*Z*[*\*]*^*_*`*a*b*c*d*e*f*g*h*i*j*k*l*m*n*o*p*q*r*s*t*u*v*w*x*y*z*{*|*}*~*****************GCOL nononononononoyes yes no no no nononoyesyesnonoyesnoyesnoyesnoyesyesnoyesnono no!no"no#no$yes%yes&no'yes(no)no*yes+no,yes-yes.no/no0no1no2no3no4no5yes6no7yes8no9no:yes;no<no=no>no?no@yesAnoByesCyesDnoEnoFnoGnoHnoInoJnoKnoLnoMyesNnoOnoPnoQnoRnoSyesTnoUyesVnoWnoXnoYyesZno[no\no]no^yes_yes`yesayesbnocnodnoeyesfnognohnoiyesjyeskyeslyesmnonnoonopnoqyesryessnotnounovnownoxnoynozno{no|yes}yes~yesnoyesnononoyesnonoyesnononoyesnoyesnononononoyesnonononononoyesyesyesyesnonoyesnoyesnonononononononononononoyesnonoyesyesnononononononononoyesnononononoyesnonononononononononononononononononoyesnononononoyesnonononoyesnononoyesnonononoyesyesnononoyesyesnonononononononononononononoyesyesnononononononono no no yes no nonononononononononononononononononoyes no!no"yes#no$no%no&no'yes(no)no*no+no,no-no.yes/nan0no1no2yes3no4no5no6no7yes8no9nan:no;no<no=no>no?no@noAnoBnoCnoDnoEnoFyesGnoHnoInoJnoKyesLnoMnoNnoOnoPnoQnoRnoSnoTnoRLLеLL xLirradiationnode-capsmenopausebreast-quadrecurrenceint1breastint3int2int00LHEAPX(@Qnamesorderingtypes0cccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccZc[c\c]c^c_c`cacbcccdcecfcgchcicjckclcmcncocpcqcrcsctcucvcwcxcyczc{c|c}c~cccccccccccccccccccccccccccccccccc** *!*"*#*$*%*&*'*(*)***+*,*-*.*/*0*1*2*3*4*5*6*7*8*9*:*;*<*=*>*?*@*A*B*C*D*E*F*G*H*I*J*K*L*M*N*O*P*Q*R*S*Tccccccccc c c c c ccccccccccccccccccc c!c"c#c$c%c&c'c(c)c*c+c,c-c.c/c0c1c2c3c4c5c6c7c8c9c:c;c<c=c>c?c@cAcBcCcDcEcFcGcHcIcJcKcLcMcNcOcPcQcRcScTcUcVcWcXcYGCOL nonoyesyesnononono no yes no yes yesnoyesnonononononononannonononoyesnonono yes!no"yes#no$yes%no&no'yes(no)yes*yes+no,no-no.no/yes0no1no2no3yes4nan5no6no7no8yes9no:no;no<yes=no>yes?yes@noAnoBnoCnoDnoEnoFyesGnoHnoInoJnoKyesLnoMyesNyesOnanPnoQnoRnoSnoTnoUyesVnoWyesXnoYnoZno[no\no]no^no_no`noanobnocnodnaneyesfyesgyeshnoinojnoknolyesmnonnanonopnoqnornosnotnounovnownoxnoyyeszno{no|yes}no~nononoyesnonononononononononoyesyesyesnonononononononoyesnoyesnononoyesnononononoyesnononononoyesnoyesnononononononononanyesnonononononononononoyesnononoyesnoyesnonoyesnononononononononononoyesnononoyesnonononononononononononononono premenopausal>=40>=40 premenopausal>=40>=40 premenopausal>=40 premenopausal premenopausal premenopausal>=40>=40>=40<40 premenopausal>=40>=40 premenopausal premenopausal>=40 premenopausal>=40>=40>=40>=40 premenopausal premenopausal premenopausal premenopausal>=40>=40 >=40 >=40  premenopausal  premenopausal  premenopausal>=40>=40 premenopausal premenopausal premenopausal premenopausal>=40 premenopausal premenopausal<40>=40 premenopausal>=40>=40 premenopausal>=40 premenopausal premenopausal  premenopausal! premenopausal">=40#>=40$>=40% premenopausal&>=40' premenopausal( premenopausal) premenopausal* premenopausal+ premenopausal, premenopausal- premenopausal.>=40/>=400 premenopausal1>=402 premenopausal3>=404>=405>=406 premenopausal7 premenopausal8<409 premenopausal: premenopausal; premenopausal< premenopausal= premenopausal> premenopausal? premenopausal@>=40A premenopausalB>=40C premenopausalD>=40 Е Е Е ЕЕЕЕ Е ЕЕЕ Е Е Е Е ЕЕ Е ЕЕЕЕЕ Е Е Е Е Е Е ЕЕ Е ЕЕ Е Е Е| Е} Е~ ЕЕ Е Е ЕЕ ЕЕЕЕЕЕЕ ЕЕЕЕЕ ЕcDЕЕ ЕЕ ЕЕ Е ЕЕЕЕЕЕ Е5Е6Е7 Е8 Е9Е:Е; Е<Е= Е>Е? 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