Cost-Sensitive Semi-Supervised Support Vector Machine

In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as a patient. Also, the acquisition of labeled data requires medical diagnosis which is expensive, while the collection of unlabeled data such as basic health information is much cheaper. We propose the CS4VM (Cost-Sensitive Semi-Supervised Support Vector Machine) to address this problem. We show that the CS4VM, when given the label means of the unlabeled data, closely approximates the supervised cost-sensitive SVM that has access to the ground-truth labels of all the unlabeled data. This observation leads to an efficient algorithm which first estimates the label means and then trains the CS4VM wit...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou
• Keywords: AAAI 2010, Cost-sensitive Semi-supervised Learning, Cost-Sensitive Semi-Supervised Support, Intelligent Agents, Unlabeled Data