A semi-supervised hierarchical classifier based on local information

The scarcity of labeled data is a common problem in supervised classification and in particular in hierarchical classification. Therefore, in this work a semi-supervised hierarchical classifier based on local information (SSHC-BLI) is proposed in order to take advantage of labeled and unlabeled data to perform classification tasks. SSHC-BLI is a semi-supervised learning algorithm for hierarchical classification, which tries to pseudo-label each unlabeled instance using the labels of its labeled neighbors, also, it uses a similarity function to determine whether the unlabeled instance is similar to its labeled neighbors to be pseudo-labeled; in this way, the heuristic function similarity of an instance with a set of instances is proposed. SSHC-BLI was tested in several datasets from different fields, including: artificial, functional genomics and text; also, it was compared against a supervised hierarchical classifier and  two state of the art methods, showing in most cases superior performance with statistical significance in exact match and Matthews correlation coefficient.