JStumps: boosting of stumps in Java
JStumps implements in Java the boosting of stumps. Many options are available and described below.
Download the JStump archive here.
Examples
JStumps program
Learning in verbose mode
# java JStumps --file=datasets/breast-cancer/CrossValidation/shot_1 --boost=20
. parameters used:
help = false
verb = true
save = false
test = false
isoclass = false
file = /home/torre/datasets/breast-cancer/shot_1
insist = 10
onthefly = false
boost = 20
showst = false
. reading names file: /home/torre/datasets/breast-cancer/shot_1.names
. reading data file: /home/torre/datasets/breast-cancer/shot_1.data
. 20 steps of boosting now
. 18 stumps found
. error rate on data file: 3,97 %
Learn and test, not verbose
# java JStumps --file=datasets/breast-cancer/CrossValidation/shot_1 --boost=20 --noverb --test . error rate on test file: 4,35 %
Learn and save classifier
# java JStumps --file=datasets/breast-cancer/CrossValidation/shot_1 --boost=20 --noverb --save
JSConsult program
Consult classifier
# java JSConsult --classifier=datasets/breast-cancer/CrossValidation/shot_1.JStumps --noverb --showst if UniformityCellSize<=3.0 then class 2 else class 4 [weight=0.17779300366525003] if ClumpThickness<=6.0 then class 2 else class 4 [weight=0.13548074159737228] if BareNuclei<=1.0 then class 2 else class 4 [weight=0.10766897146164137] if UniformityCellSize<=3.0 then class 4 else class 2 [weight=0.09351568652457329] if BlandChromatin<=2.0 then class 2 else class 4 [weight=0.06902295191295167] if NormalNucleoli<=3.0 then class 2 else class 4 [weight=0.04925353047208494] if BlandChromatin<=4.0 then class 2 else class 4 [weight=0.047019527580647674] if MarginalAdhesion<=1.0 then class 2 else class 4 [weight=0.0361683906745061] if Mitoses<=1.0 then class 2 else class 4 [weight=0.034611098925205766] if UniformityCellShape<=2.0 then class 2 else class 4 [weight=0.03309250317288488] if ClumpThickness<=2.0 then class 2 else class 4 [weight=0.03203251573592544] if ClumpThickness<=4.0 then class 2 else class 4 [weight=0.03196929631350762] if NormalNucleoli<=1.0 then class 4 else class 2 [weight=0.029934655990969988] if ClumpThickness<=5.0 then class 4 else class 2 [weight=0.028165092442751286] if BareNuclei<=3.0 then class 2 else class 4 [weight=0.02730910998261713] if SingleEpithelialCellSize<=1.0 then class 4 else class 2 [weight=0.023181867572012904] if SingleEpithelialCellSize<=2.0 then class 2 else class 4 [weight=0.02212647398009039] if MarginalAdhesion<=9.0 then class 2 else class 4 [weight=0.021654581995007294]
Classify test examples
# java JSConsult --classifier=datasets/breast-cancer/CrossValidation/shot_1.JStumps
--file=datasets/breast-cancer/CrossValidation/shot_1 --noverb --predictions
test1,4,0.41566580533981323,4
test2,4,0.5130572319030762,4
test3,4,0.09624868631362915,4
test4,4,0.3285263776779175,4
test5,2,0.24965348839759827,4
test6,4,0.3390907943248749,4
test7,4,0.1266273856163025,4
test8,4,0.12250885367393494,4
test9,4,0.607096254825592,4
test10,4,0.44383499026298523,4
test11,4,0.5130572319030762,4
test12,4,0.3924649953842163,4
test13,4,0.6504054665565491,4
test14,4,0.5563663840293884,4
test15,4,0.2863062620162964,4
.............................
Output confusion matrix
# java JSConsult --classifier=datasets/breast-cancer/CrossValidation/shot_1.JStumps
--file=datasets/breast-cancer/CrossValidation/shot_1 --noverb --confusion
;2,2,43;2,4,1;4,2,2;4,4,23
More informations
- see the Javadoc
- use java JStumps --help
- use java JSConsult --help
- works for learning problems with two classes to predict only
- missing values are authorized in both training set and test set
- JStumps uses C4.5 formats (.names, .data and .test)
- requires Java 6 (downloads the latest JDK)
Bibliography
Definition of stumps (also called AttrTest, section 3.1):
Boosting algorithm used in this implementation (Adaboost, figure 1):
