Complete results of the experimentation

IPMU2020

Description

In this file the complete experimental results are shown for the work: 

        Serafín Moral-García, Carlos J. Mantas, Javier G. Castellano and Joaquin Abellán
        Imprecise Classification with Non-Parametric Predictive Inference
        Submitted to IPMU2020.


Complete DACC results

Dataset ICDT-NPI ICDT-IDM1 ICDT-IDM2 ICDT-IDM3
anneal 0.9835 0.9957 0.9810 0.9798
arrhythmia 0.6699 0.6625 0.6736 0.6666
audiology 0.8021 0.7887 0.7407 0.7057
autos 0.7256 0.7817 0.6905 0.6469
balance-scale 0.7121 0.6991 0.7121 0.7293
bridges-version1 0.6403 0.6375 0.6178 0.5908
bridges-version2 0.6134 0.5729 0.6012 0.6194
car 0.9016 0.9168 0.8715 0.8580
cmc 0.4897 0.4884 0.4897 0.4858
dermatology 0.9186 0.9405 0.9275 0.8971
ecoli 0.7904 0.7993 0.7891 0.7829
flags 0.5499 0.5554 0.5646 0.5601
hypotiroid 0.9914 0.9435 0.9901 0.9871
iris 0.9376 0.9337 0.9420 0.9458
letter 0.7427 0.7714 0.7226 0.6920
lymphography 0.7393 0.7275 0.7277 0.7428
mfeat-pixel 0.7592 0.7702 0.7560 0.7345
nursery 0.9491 0.9628 0.9375 0.9226
optdigits 0.7498 0.7716 0.7476 0.7232
page-blocks 0.9584 0.9619 0.9575 0.9549
pendingitis 0.8659 0.8812 0.8547 0.8355
Post-operative-patient-data 0.7111 0.7104 0.7111 0.7111
Primary-tumor 0.3818 0.3815 0.3685 0.3600
segment 0.9290 0.9406 0.9263 0.9137
soybean 0.9137 0.9178 0.8816 0.9003
spectrometer 0.4012 0.4430 0.4158 0.3718
splice 0.9242 0.9270 0.9315 0.9268
sponge 0.9220 0.9293 0.9321 0.9325
tae 0.4661 0.4678 0.4661 0.4535
vehicle 0.6830 0.6899 0.6852 0.6739
vowel 0.6967 0.7635 0.6595 0.5811
waveform 0.7334 0.7371 0.7386 0.7353
wine 0.9166 0.9194 0.9297 0.9267
zoo 0.9268 0.9592 0.9213 0.8927
Average 0.7675 0.7763 0.7606 0.7482


Complete MIC results

Dataset ICDT-NPI ICDT-IDM1 ICDT-IDM2 ICDT-IDM3
anneal 1.7563 1.7825 1.7510 1.7492
arrhythmia 1.8069 1.7861 1.8351 1.8208
audiology 2.5218 2.5156 2.3494 2.2268
autos 1.3259 1.4535 1.2525 1.1525
balance-scale 0.6433 0.6033 0.6433 0.6793
bridges-version1 1.0232 1.0247 0.9967 0.9517
bridges-version2 0.9699 0.8755 0.9512 1.0149
car 1.2126 1.2330 1.1587 1.3686
cmc 0.2688 0.2599 0.2696 0.2667
dermatology 1.6167 1.6637 1.6390 1.5710
ecoli 1.6030 1.6128 1.6007 1.5859
flags 1.0206 1.0322 1.0667 1.0749
hypotiroid 1.3705 1.3744 1.3681 1.3626
iris 0.9992 0.9911 1.0060 1.0138
letter 2.3937 2.5135 2.3863 2.2944
lymphography 0.9072 0.8857 0.8960 0.9322
mfeat-pixel 1.6865 1.7194 1.6913 1.6403
nursery 1.5072 1.5332 1.5250 1.5207
optdigits 1.6629 1.7275 1.6714 1.6206
page-blocks 1.5263 1.5332 1.5250 1.5207
pendingitis 1.9617 2.0042 1.9415 1.8979
Post-operative-patient-data 0.6225 0.6213 0.6225 0.6225
Primary-tumor 1.1696 1.1476 1.1475 1.1314
segment 1.7879 1.8119 1.7807 1.7579
soybean 2.6872 2.7004 2.6133 2.6540
spectrometer 1.5609 1.7353 1.7117 1.5977
splice 0.9737 0.9784 0.9862 0.9786
sponge 0.9701 0.9822 0.9867 0.9874
tae 0.2196 0.2218 0.2196 0.2037
vehicle 0.8079 0.8171 0.8130 0.7990
vowel 1.6173 1.7889 1.5493 1.3739
waveform 0.6611 0.6656 0.6702 0.6664
wine 0.9615 0.9658 0.9828 0.9779
zoo 1.7797 1.8532 1.7673 1.7024
Average 1.3414 1.3652 1.3334 1.3065


Any questions?

Please feel free to contact me if you have any questions: seramoral@decsai.ugr.es