APSSCAmerican Psychological Society Student Caucus
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Subsequently, we compared the proposed method with other state-of-the-art algorithms into the KEEL tool [39] such as self-training (C45) [7], self-training (SMO) [40], self-training (NN) [32], SETRED [13], cotraining (C45) [14], cotraining (SMO) [41], democratic-co [27], tri-training (C45) [41], tri-training (SMO) [25], tri-training (NN) [41], DE-tri-training (C45), DE-tri-training (SMO) [42], Co-Forest [6], Rasco (C45) [23], CLCC [29], APSSC [30], SNNRCE [31], Rel-Rasco (NB) [24], ADE-Co-Forest [43], cobagging (C45) [22], and cobagging (SMO) [44].
In this experiment, self-trained LMT and Co-Forest presented 8 wins in an amount of 52 datasets, being followed by self-training (C45), cotraining (C45), and APSSC with 5 victories.
Furthermore, many other algorithms, such as Rel-Rasco (NB), APSSC, and de-tri-training (SMO), did not manage to achieve a noteworthy improvement between 30% and 40% labeled ratio.