document: Exploiting unlabeled data in ensemble methods An adaptive semi-supervised ensemble method , ASSEMBLE , is proposed that constructs classification ensembles based on both labeled and unlabeled data . ASSEMBLE alternates between assigning `` pseudo-classes '' to the unlabeled data using the existing ensemble and constructing the next base classifier using both the labeled and pseudolabeled data . Mathematically , this intuitive algorithm corresponds to maximizing the classification margin in hypothesis space as measured on both the labeled and unlabeled of data . Unlike alternative approaches , ASSEMBLE does not require a semi-supervised learning method for the base classifier . ASSEMBLE can be used in conjunction with any cost-sensitive classification algorithm for both two-class and multi-class problems . ASSEMBLE using decision trees won the NIPS 2001 Unlabeled Data Competition . In addition , strong results on several benchmark datasets using both decision trees and neural networks support the proposed method .
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