Constrained Multi-Label Classification: A Semidefinite Programming Approach
نویسندگان
چکیده
Multi-label classification is more general in practice because it allows one instance to have more than one label simultaneously. In this paper, we focus on one type of multilabel classification in that there exist constraints among the labels. We formulate this kind of multi-label classification into a minimum cut problem, where all labels and their correlations are represented by a weighted graph. To attain the solutions of the minimum cut problem, we propose a semidefinite programming (SDP) approach. The experimental evaluation results show that our multi-label classification approach works much better than SVM+BR method.
منابع مشابه
A semidefinite relaxation scheme for quadratically constrained
Semidefinite optimization relaxations are among the widely used approaches to find global optimal or approximate solutions for many nonconvex problems. Here, we consider a specific quadratically constrained quadratic problem with an additional linear constraint. We prove that under certain conditions the semidefinite relaxation approach enables us to find a global optimal solution of the unde...
متن کاملExploiting Associations between Class Labels in Multi-label Classification
Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...
متن کاملModel and Solution Approach for Multi objective-multi commodity Capacitated Arc Routing Problem with Fuzzy Demand
The capacitated arc routing problem (CARP) is one of the most important routing problems with many applications in real world situations. In some real applications such as urban waste collection and etc., decision makers have to consider more than one objective and investigate the problem under uncertain situations where required edges have demand for more than one type of commodity. So, in thi...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملSemidefinite and Spectral Relaxations for Multi-Label Classification
In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior takes the form of a quadratic function of the labels and permits to encode both attractive and repulsive relations between labels. We cast this problem as a structured prediction...
متن کامل