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AWARE

Adaptive Weighted voting AggRegation for Ensemble of classifiers.

Random Forest (RF) is a successful technique of ensemble prediction that uses the majority voting. However, it is clear that each tree in a random forest can have different contribution to the treatment of some instance. So we need to replace the classical ordinary vote by the weighted one with local performance of each tree, this choice is justified by the fact that the classical vote gives equal weight to each decision of each tree and depends on the choice of a majority of classifiers that give the same class for databases, while the trees do not have the same performance.

In this repo, by the inspiration of AdaBoost, I proposed a new way to calculate weights and named it AWARE. Experiments also indicate that this weighted voting method gives better results compared to the majority vote (RF) and all the other weighted voting methods (like TWRF, WAVE, DIRF).

See mean accuracy (%) results in the Table below. It outperforms almost every methods.

Dataset RF TWRF WAVE DIRF AWARE
breast 95.4631 95.4368 95.4725 95.3852 95.6179🏅️
car 70.0239 70.0239 70.0436 70.0556 78.2606🏅️
credit 70.0220 70.0260 70.1460 70.8000 72.9940🏅️
ecoli 73.3853 75.1242 76.3744 75.3214 78.7455
forest 87.9065 87.9636 87.9657🏅️ 87.8320 87.4520
glass 87.1992 92.3619 92.1038 92.5926🏅️ 90.7088
hcv 90.4296 90.4476 90.4831 90.5297 91.9214🏅️
image 92.7463 92.9190 93.1853 93.5986 95.5368🏅️
immuno 78.6778 78.7333 78.7444 79.3043 79.9667🏅️
letter 69.9882 70.2906 70.4752 70.8571🏅️ 70.3827
liver 67.7068 67.7778 67.8953🏅️ 67.3678 67.7829
nursery 58.9018 72.6107 78.8792 80.7143 83.7187🏅️
parkinsons 87.8452 87.7895 88.0123 87.551 89.5794🏅️
shuttle 99.7112 99.7227 99.7473 99.7342 99.8384🏅️
sonar 80.1979 80.0850 80.2457 80.7036 81.0457🏅️
thoraric 85.1064 85.1064 85.1064 86.4407🏅️ 83.6915
tic-tac-toe 70.7223 70.8258 71.3973 78.7500 83.0315🏅️
transfusion 77.1141 77.1369 77.1558 77.4606🏅️ 75.4813
waveform 82.0916 82.1916 82.3948 82.6400 83.3883🏅️
wilt 94.6069 94.6069 94.9126 94.6802 97.7844🏅️

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