In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and discussed. Multi-label classification is a superset of traditional binary and multiclass classification problems. The proposed work extends the extreme learning machine technique to adapt to the multi-label problems. As opposed to the singlelabel problem...

Existing research on categories has only examined indirectly the value associated with being a member of a category relative to the value of the set of attributes that determine membership in that category. This study uses survey data to analyze consumers' preferences for the "organic" label versus for the attributes underlying that label. We found that consumers generally preferred products wi...

Multi-label learning has become an important area of research due to the increasing number of modern applications that contain multi-label data. The multi-label data are structured in a more complex way than single-label data. Consequently the development of techniques that allow the improvement in the performance of machine learning algorithms over multi-label data is desired. The feature weig...

Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-...

BACKGROUND Many barriers exist to conducting pediatric cardiovascular (CV) trials, and the majority of therapies used are not evidence based. Recent legislation has aimed to stimulate pediatric research and improve drug labeling. This study describes off-label use of CV medications in children hospitalized with congenital and acquired CV disease. METHODS AND RESULTS The 2005 Pediatric Health ...

Multi-label problems arise in different domains such as digital media analysis and description, text categorization, multi-topic web page categorization, image and video annotation etc. Such a situation arises when the data are associated with multiple labels simultaneously. Similar to single label problems, multi label problems also suffer from high dimensionality as multi label data often hap...

High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free a...

For more than 20 years the off-label use of drugs has been an essential part of the ethical and legal considerations regarding the international regulation of drug licensing. Despite a number of regulatory initiatives in the European Union, there seems to remain a largely unsatisfactory situation following a number of critical descriptions and statements from actors in the field. The present ar...

Multi-label conformal prediction has attracted much attention in the conformal predictor (CP) community. In this article, we propose to combine CP with random k -labelsets (RAkEL) method, which is state-of-the-art multi-label classification method for large number of labels. In the framework of RAkEL, the original problem is reduced to a number of small-sized multi-label classification tasks by...

We present a scalable Bayesian multi-label learning model based on learning lowdimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic being a distribution over labels), where the combination weights (i.e., the embeddings) for each label vector are conditioned on the observed feature vector. This construction, ...