Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning

Unsupervised models can provide supplementary soft constraints to help classify new target data under the assumption that similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place. This paper describes a Bayesian framework that takes as input class labels from existing classifiers (designed based on labeled data from the source domain), as well as cluster labels from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. This framework is particularly useful when the statistics of the target data drift or change from those of the training data. We also show that the proposed framework is privacy-aware and allows performing distributed learning when data/models have sharing restrictions. Experiments show that our framework can yield superior results to those provided by applying classifier ensembles only

Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning

Unsupervised models can provide supplementary soft constraints to help classify new, target data since similar objects in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place. This paper describes a Bayesian framework that takes as input class labels from existing classifiers, as well as cluster labels from a cluster ensemble operating solely on the target data to be classified, and yields a consensus labeling of the target data. Classifiers are first designed based on labeled data and subsequently, when unlabeled target data is available, the existing classifiers can be effectively applied to it with the aid of a cluster ensemble. This framework is particularly useful when the statistics of the target data drift or change from those of the training data. We also show that the proposed framework is privacy-aware and allows performing transductive learning even when data/models are distributed and have sharing restrictions. A variety of experiments with real-world data show that our framework can yield superior results to those provided by applying classifier ensembles only.