Three transductive set covering machines

Abstract

We propose three transductive versions of the set covering machine with data dependent rays for classification in the molecular high-throughput setting. Utilizing both labeled and unlabeled samples, these transductive classifiers can learn information from both sample types, not only from labeled ones. These transductive set covering machines are based on modified selection criteria for their ensemble members. Via counting arguments we include the unlabeled information into the base classifier selection. One of the three methods we developed, uniformly increased the classification accuracy, the other two showed mixed behaviour for all data sets. Here, we could show that only by observing the order of unlabeled samples, not distances, we were able to increase classification accuracies, making these approaches useful even when very few information is available.