% Date: Tue, 15 Nov 88 15:44:08 EST % From: stan % To: aha@ICS.UCI.EDU % % 1. Title: Final settlements in labor negotitions in Canadian industry % % 2. Source Information % -- Creators: Collective Barganing Review, montly publication, % Labour Canada, Industrial Relations Information Service, % Ottawa, Ontario, K1A 0J2, Canada, (819) 997-3117 % The data includes all collective agreements reached % in the business and personal services sector for locals % with at least 500 members (teachers, nurses, university % staff, police, etc) in Canada in 87 and first quarter of 88. % -- Donor: Stan Matwin, Computer Science Dept, University of Ottawa, % 34 Somerset East, K1N 9B4, (stan@uotcsi2.bitnet) % -- Date: November 1988 % % 3. Past Usage: % -- testing concept learning software, in particular % an experimental method to learn two-tiered concept descriptions. % The data was used to learn the description of an acceptable % and unacceptable contract. % The unacceptable contracts were either obtained by interviewing % experts, or by inventing near misses. % Examples of use are described in: % Bergadano, F., Matwin, S., Michalski, R., % Zhang, J., Measuring Quality of Concept Descriptions, % Procs. of the 3rd European Working Sessions on Learning, % Glasgow, October 1988. % Bergadano, F., Matwin, S., Michalski, R., Zhang, J., % Representing and Acquiring Imprecise and Context-dependent % Concepts in Knowledge-based Systems, Procs. of ISMIS'88, % North Holland, 1988. % 4. Relevant Information: % -- data was used to test 2tier approach with learning % from positive and negative examples % % 5. Number of Instances: 57 % % 6. Number of Attributes: 16 % % 7. Attribute Information: % 1. dur: duration of agreement % [1..7] % 2 wage1.wage : wage increase in first year of contract % [2.0 .. 7.0] % 3 wage2.wage : wage increase in second year of contract % [2.0 .. 7.0] % 4 wage3.wage : wage increase in third year of contract % [2.0 .. 7.0] % 5 cola : cost of living allowance % [none, tcf, tc] % 6 hours.hrs : number of working hours during week % [35 .. 40] % 7 pension : employer contributions to pension plan % [none, ret_allw, empl_contr] % 8 stby_pay : standby pay % [2 .. 25] % 9 shift_diff : shift differencial : supplement for work on II and III shift % [1 .. 25] % 10 educ_allw.boolean : education allowance % [true false] % 11 holidays : number of statutory holidays % [9 .. 15] % 12 vacation : number of paid vacation days % [ba, avg, gnr] % 13 lngtrm_disabil.boolean : % employer's help during employee longterm disabil % ity [true , false] % 14 dntl_ins : employers contribution towards the dental plan % [none, half, full] % 15 bereavement.boolean : employer's financial contribution towards the % covering the costs of bereavement % [true , false] % 16 empl_hplan : employer's contribution towards the health plan % [none, half, full] % % 8. Missing Attribute Values: None % % 9. Class Distribution: % % 10. Exceptions from format instructions: no commas between attribute values. % % @relation 'labor-neg-data' @attribute 'duration' CONTINUOUS @attribute 'wage-increase-first-year' CONTINUOUS @attribute 'wage-increase-second-year' CONTINUOUS @attribute 'wage-increase-third-year' CONTINUOUS @attribute 'cost-of-living-adjustment' {'none','tcf','tc'} @attribute 'working-hours' CONTINUOUS @attribute 'pension' {'none','ret_allw','empl_contr'} @attribute 'standby-pay' CONTINUOUS @attribute 'shift-differential' CONTINUOUS @attribute 'education-allowance' {'yes','no'} @attribute 'statutory-holidays' CONTINUOUS @attribute 'vacation' {'below_average','average','generous'} @attribute 'longterm-disability-assistance' {'yes','no'} @attribute 'contribution-to-dental-plan' {'none','half','full'} @attribute 'bereavement-assistance' {'yes','no'} @attribute 'contribution-to-health-plan' {'none','half','full'} @attribute 'class' {'bad','good'} @data 1,5,?,?,?,40,?,?,2,?,11,'average',?,?,'yes',?,'good' 2,4.5,5.8,?,?,35,'ret_allw',?,?,'yes',11,'below_average',?,'full',?,'full','good' ?,?,?,?,?,38,'empl_contr',?,5,?,11,'generous','yes','half','yes','half','good' 3,3.7,4,5,'tc',?,?,?,?,'yes',?,?,?,?,'yes',?,'good' 3,4.5,4.5,5,?,40,?,?,?,?,12,'average',?,'half','yes','half','good' 2,2,2.5,?,?,35,?,?,6,'yes',12,'average',?,?,?,?,'good' 3,4,5,5,'tc',?,'empl_contr',?,?,?,12,'generous','yes','none','yes','half','good' 3,6.9,4.8,2.3,?,40,?,?,3,?,12,'below_average',?,?,?,?,'good' 2,3,7,?,?,38,?,12,25,'yes',11,'below_average','yes','half','yes',?,'good' 1,5.7,?,?,'none',40,'empl_contr',?,4,?,11,'generous','yes','full',?,?,'good' 3,3.5,4,4.6,'none',36,?,?,3,?,13,'generous',?,?,'yes','full','good' 2,6.4,6.4,?,?,38,?,?,4,?,15,?,?,'full',?,?,'good' 2,3.5,4,?,'none',40,?,?,2,'no',10,'below_average','no','half',?,'half','bad' 3,3.5,4,5.1,'tcf',37,?,?,4,?,13,'generous',?,'full','yes','full','good' 1,3,?,?,'none',36,?,?,10,'no',11,'generous',?,?,?,?,'good' 2,4.5,4,?,'none',37,'empl_contr',?,?,?,11,'average',?,'full','yes',?,'good' 1,2.8,?,?,?,35,?,?,2,?,12,'below_average',?,?,?,?,'good' 1,2.1,?,?,'tc',40,'ret_allw',2,3,'no',9,'below_average','yes','half',?,'none','bad' 1,2,?,?,'none',38,'none',?,?,'yes',11,'average','no','none','no','none','bad' 2,4,5,?,'tcf',35,?,13,5,?,15,'generous',?,?,?,?,'good' 2,4.3,4.4,?,?,38,?,?,4,?,12,'generous',?,'full',?,'full','good' 2,2.5,3,?,?,40,'none',?,?,?,11,'below_average',?,?,?,?,'bad' 3,3.5,4,4.6,'tcf',27,?,?,?,?,?,?,?,?,?,?,'good' 2,4.5,4,?,?,40,?,?,4,?,10,'generous',?,'half',?,'full','good' 1,6,?,?,?,38,?,8,3,?,9,'generous',?,?,?,?,'good' 3,2,2,2,'none',40,'none',?,?,?,10,'below_average',?,'half','yes','full','bad' 2,4.5,4.5,?,'tcf',?,?,?,?,'yes',10,'below_average','yes','none',?,'half','good' 2,3,3,?,'none',33,?,?,?,'yes',12,'generous',?,?,'yes','full','good' 2,5,4,?,'none',37,?,?,5,'no',11,'below_average','yes','full','yes','full','good' 3,2,2.5,?,?,35,'none',?,?,?,10,'average',?,?,'yes','full','bad' 3,4.5,4.5,5,'none',40,?,?,?,'no',11,'average',?,'half',?,?,'good' 3,3,2,2.5,'tc',40,'none',?,5,'no',10,'below_average','yes','half','yes','full','bad' 2,2.5,2.5,?,?,38,'empl_contr',?,?,?,10,'average',?,?,?,?,'bad' 2,4,5,?,'none',40,'none',?,3,'no',10,'below_average','no','none',?,'none','bad' 3,2,2.5,2.1,'tc',40,'none',2,1,'no',10,'below_average','no','half','yes','full','bad' 2,2,2,?,'none',40,'none',?,?,'no',11,'average','yes','none','yes','full','bad' 1,2,?,?,'tc',40,'ret_allw',4,0,'no',11,'generous','no','none','no','none','bad' 1,2.8,?,?,'none',38,'empl_contr',2,3,'no',9,'below_average','yes','half',?,'none','bad' 3,2,2.5,2,?,37,'empl_contr',?,?,?,10,'average',?,?,'yes','none','bad' 2,4.5,4,?,'none',40,?,?,4,?,12,'average','yes','full','yes','half','good' 1,4,?,?,'none',?,'none',?,?,'yes',11,'average','no','none','no','none','bad' 2,2,3,?,'none',38,'empl_contr',?,?,'yes',12,'generous','yes','none','yes','full','bad' 2,2.5,2.5,?,'tc',39,'empl_contr',?,?,?,12,'average',?,?,'yes',?,'bad' 2,2.5,3,?,'tcf',40,'none',?,?,?,11,'below_average',?,?,'yes',?,'bad' 2,4,4,?,'none',40,'none',?,3,?,10,'below_average','no','none',?,'none','bad' 2,4.5,4,?,?,40,?,?,2,'no',10,'below_average','no','half',?,'half','bad' 2,4.5,4,?,'none',40,?,?,5,?,11,'average',?,'full','yes','full','good' 2,4.6,4.6,?,'tcf',38,?,?,?,?,?,?,'yes','half',?,'half','good' 2,5,4.5,?,'none',38,?,14,5,?,11,'below_average','yes',?,?,'full','good' 2,5.7,4.5,?,'none',40,'ret_allw',?,?,?,11,'average','yes','full','yes','full','good' 2,7,5.3,?,?,?,?,?,?,?,11,?,'yes','full',?,?,'good' 3,2,3,?,'tcf',?,'empl_contr',?,?,'yes',?,?,'yes','half','yes',?,'good' 3,3.5,4,4.5,'tcf',35,?,?,?,?,13,'generous',?,?,'yes','full','good' 3,4,3.5,?,'none',40,'empl_contr',?,6,?,11,'average','yes','full',?,'full','good' 3,5,4.4,?,'none',38,'empl_contr',10,6,?,11,'generous','yes',?,?,'full','good' 3,5,5,5,?,40,?,?,?,?,12,'average',?,'half','yes','half','good' 3,6,6,4,?,35,?,?,14,?,9,'generous','yes','full','yes','full','good' % % %