Collective Loss Function for Positive and Unlabeled Learning
People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods.
Code (0)
등록된 구현이 없습니다.
Tasks
CPUSimilar Papers 제목 키워드 기반
Analysis of Learning from Positive and Unlabeled Data
Learning a classifier from positive and unlabeled data is an important class of classification problems that are conceivable in many practical applications. In this paper, we first show that this problem can be solved by…
General ClassificationOutlier DetectionPUAL: A Classifier on Trifurcate Positive-Unlabeled Data
Positive-unlabeled (PU) learning aims to train a classifier using the data containing only labeled-positive instances and unlabeled instances. However, existing PU learning methods are generally hard to achieve satisfact…
Relaxed Softmax for learning from Positive and Unlabeled data
In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss has been extended to language modeling a…
Density EstimationLanguage ModelingLanguage ModellingD-GAN: Divergent generative adversarial network for positive unlabeled learning and counter-examples generation
Positive Unlabeled (PU) learning consists in learning to distinguish samples of our class of interest, the positive class, from the counter-examples, the negative class, by using positive labeled and unlabeled samples du…
Generative Adversarial NetworkGenerating Relevant Counter-Examples from a Positive Unlabeled Dataset for Image Classification
With surge of available but unlabeled data, Positive Unlabeled (PU) learning is becoming a thriving challenge. This work deals with this demanding task for which recent GAN-based PU approaches have demonstrated promising…
General Classificationimage-classificationImage Classification