Max-Coupled Learning: Application to Breast Cancer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Cardoso, JS (Autor) FEUP; Domingues, I (Autor) Outra

Ata de Conferência Internacional

Título: Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011

Páginas: 13-18

10th International Conference on Machine Learning and Applications, ICMLA 2011

Honolulu, HI, 18 December 2011 through 21 December 2011

Indexação

ISI Web of Knowledge

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-008-2X3

DOI: 10.1109/ICMLA.2011.93

Abstract (EN): In the predictive modeling tasks, a clear distinction is often made between learning problems that are supervised or unsupervised, the first involving only labeled data (training patterns with known category labels) while the latter involving only unlabeled data. There is a growing interest in a hybrid setting, called semi-supervised learning, in semi-supervised classification, the labels of only a small portion of the training data set are available. The unlabeled data, instead of being discarded, are also used in the learning process. Motivated by a breast cancer application, in this work we address a new learning task, in-between classification and semi-supervised classification. Each example is described using two different feature sets, not necessarily both observed for a given example. If a single view is observed, then the class is only due to that feature set, if both views are present the observed class label is the maximum of the two values corresponding to the individual views. We propose new learning methodologies adapted to this learning paradigm and experimentally compare them with baseline methods from the conventional supervised and unsupervised settings. The experimental results verify the usefulness of the proposed approaches. © 2011 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica