Boosted Test-FDA: a transductive boosting method

Abstract

Advantages of machine learning algorithms are compared in terms of “generalization,” which means how accurate they perform upon unseen data. There are many applications in which test data are available (obviously without target). A central question with regard to “generalization” is as follows: Would it be possible to deploy test feature vectors in the training phase? For test data, nonetheless, there is no target—this is the paradox. Transductive learning is a form of learning that uses test data in addition to train data in the training phase. Using decision tree as the base model reveals many errors occur on those data, the feature values of which, are very close to splitting values of decision tree’s nodes. This observation is the essence of answering the aforementioned paradox in this study. This paper proposes a combination of decision tree and a novel Fisher discriminant analysis, in which the proposed modified FDA has an embedded mechanism using test data as a shortcut to generalization. This combination further arranged in a boosting system with weights on both train and test data. Experiments are evaluated on 21 datasets from various fields. The results decisively demonstrate the promising performance of the proposed method.