A method on selecting reliable samples based on fuzziness in positive and unlabeled learning
This addresses a specific challenge in semi-supervised learning for scenarios with limited labeled data, representing an incremental improvement over existing methods.
The paper tackles the problem of learning from only positive and unlabeled instances in semi-supervised settings by proposing a framework that selects reliable negative and positive instances based on fuzziness and filters noise, with effectiveness verified on UCI datasets.
Traditional semi-supervised learning uses only labeled instances to train a classifier and then this classifier is utilized to classify unlabeled instances, while sometimes there are only positive instances which are elements of the target concept are available in the labeled set. Our research in this paper the design of learning algorithms from positive and unlabeled instances only. Among all the semi-supervised positive and unlabeled learning methods, it is a fundamental step to extract useful information from unlabeled instances. In this paper, we design a novel framework to take advantage of valid information in unlabeled instances. In essence, this framework mainly includes that (1) selects reliable negative instances through the fuzziness of the instances; (2) chooses new positive instances based on the fuzziness of the instances to expand the initial positive set, and we named these new instances as reliable positive instances; (3) uses data editing technique to filter out noise points with high fuzziness. The effectiveness of the presented algorithm is verified by comparative experiments on UCI dataset.