Training probability transductive classifiers for group probability datasets based on regression

As an emerging and challenging learning technique, group probability classifier learning is used to train a classifier from a group probability dataset, where the class labels of each sample are unknown, but the probabilities of each class in the given data groups of the whole dataset are available. The existing work is mainly based on the inverse calibration (IC) strategy to obtain the estimated labels for data in the group probability dataset and then use the classical classification algorithms to train the desired classifier. A critical challenging for the exiting IC based method is that an idea IC function for the label estimation is not easy to design, which makes the existing methods more sensitive to the adopted IC function. In order to overcome this shortcoming, a novel probability transductive classifier is proposed without the need of IC in the learning procedure, where the probability values are directly used as the output of the training data for the model training. Particularly, on the training data with the output as the continuous real values, the existing classical regression model can be easily used to model the group probability classification problem. For a future testing data, the model output of the obtained group probability classification model will present the probability of the testing data to the positive class. The T with the given threshold, the explicit class label of the test data is obtained for classification task. The experimental results on simulated datasets and real UCI datasets show that the proposed method is more effective compared with the existing methods.