… On the other hand, using Hausdorff distance based … feature selection algorithm built on fuzzy
rough set is first proposed for dimension-reduction tasks. (2) We apply Hausdorff distance to …

… Therefore, a new composed signature feature is … feature data, the most distinctive Hausdorff
distance is further proposed to calculate the distances of the eight-dimensional feature vector …

… , two different distances; namely, Euclidean and Hausdorff distance measures are all …
clustering algorithm under Euclidean and Hausdorff distance measure was individually derived for …

… Thus, we propose two novel schemes to calculate the Hausdorff distance and its … two
forms of Hausdorff distance between SVNSs based on the definition of Hausdorff metric …

… distance established in this paper is the first work used in the world of feature selection. In
… As a result, from what has been explained about the Euclidean and Hausdorff distances, it …

… Therefore, we present a weighted hesitant Hausdorff distance for HFSs. Suppose that the
weight of every element \(x_i \in X\) is \(w_i \left( {i = 1, 2, 3, . . ., n} \right) \) such that \(\sum _{i=1…

… Hausdorff distance is inherently capable of tolerating fracture line complexity, whereas
Euclidean distance … cases; therefore, we chose Hausdorff distance as the basis on which the …

… So the proposed work recognizing object using a shape context and Hausdorff distance is
… The process evaluates the similarity of the two point set using Hausdorff distance. Finally, …

… This article analyzes the impact of six different Hausdorff distances on sets of multivariate
interval data (where, for each dimension, an interval is defined as an observation [a, b] with a …

… In this study, we present a novel parametric-based feature selection method using the three-…
The calculated similarity measurements (ie, three-dimensional Hausdorff distance (HD) [6]) …