Let G be a (p, q) graph. Let f : V (G) → {1, 2, . . . , k} be a map. For each edge uv, assign the label gcd (f(u), f(v)). f is called k-prime cordial labeling of G if |vf (i) − vf (j)| ≤ 1, i, j ∈ {1, 2, . . . , k} and |ef (0) − ef (1)| ≤ 1 where vf (x) denotes the number of vertices labeled with x, ef (1) and ef (0) respectively denote the number of edges labeled with 1 and not labeled with 1....

Let G be a (p, q) graph. Let k be an integer with 2 ≤ k ≤ p and f from V (G) to the set {1, 2, . . . , k} be a map. For each edge uv, assign the label |f(u) − f(v)|. The function f is called a k-difference cordial labeling of G if |νf (i) − vf (j)| ≤ 1 and |ef (0) − ef (1)| ≤ 1 where vf (x) denotes the number of vertices labelled with x (x ∈ {1, 2 . . . , k}), ef (1) and ef (0) respectively den...

In multi-label classification, each sample can be associated with a set of class labels. When the number of labels grows to the hundreds or even thousands, existing multi-label classification methods often become computationally inefficient. In recent years, a number of remedies have been proposed. However, they are based either on simple dimension reduction techniques or involve expensive opti...

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotat...

v 1 An Introduction to Label Placement 1 1.1 Historic Development . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 . . . and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Future Development . . . . . . . . . ....

Construction of classification models from data in practice often requires additional human effort to annotate (label) observed data instances. However, this annotation effort may often be too costly and only a limited number of data instances may be feasibly labeled. The challenge is to find methods that let us reduce the number of the labeled instances but at the same time preserve the qualit...

This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-...

Biliary stents are approved by the U.S. Food and Drug Administration only to treat biliary strictures resulting from cancer. However, these devices are often used "off-label" to treat peripheral vascular disease. This study was designed to determine the number and type of malfunctions and adverse events associated with off-label use of biliary stents in the peripheral vasculature. Confirmed bil...