The paper analyses issues leading to errors in graphic object classifiers. Thedistance measures suggested in literature and used as a basis in traditional, fuzzy, andNeuro-Fuzzy classifiers are found to be not suitable for classification of non-stylized orfuzzy objects in which the features of classes are much more difficult to recognize becauseof significant uncertainties in their location and...

Since it was firstly introduced by Torra and Narukawa (The 18th IEEE International Conference on Fuzzy Systems, Jeju Island, Korea, 2009, pp. 1378–1382), the hesitant fuzzy set has attracted more and more attention due to its powerfulness and efficiency in representing uncertainty and vagueness. This paper extends the classical VIKOR (vlsekriterijumska optimizacija i kompromisno resenje in serb...

In this article, the concept of similarity measure for intuitionistic fuzzy soft sets based on distance between two intuitionistic fuzzy soft sets, some examples and basic properties are also studied. An algorithm is developed in intuitionistic fuzzy soft set and an example is given to illustrate possible application in a medical diagnosis problem

Hellinger distance is a distance between two additive measures defined in terms of the RadonNikodym derivative of these two measures. This measure proposed in 1909 has been used in a large variety of contexts. In this paper we define an analogous measure for fuzzy measures. We discuss them for distorted probabilities and give two examples.

The Normalized Geometric and Normalized Hamming distance measures of Intuitionistic Fuzzy Multi sets (IFMS) are presented in depth in this paper. Due to the wide applications in various fields, the distance measure plays a vital role in Intuitionistic Fuzzy sets (IFS). We extend the distance measure of IFS to IFMS as there are possibilities of multi membership, non membership for the same eleme...

Abstract: Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the artic...

Record linkage is used in data privacy to evaluate the disclosure risk of protected data. It models potential attacks, where an intruder attempts to link records from the protected data to the original data. In this paper we introduce a novel distance based record linkage, which uses the Choquet integral to compute the distance between records. We use a fuzzy measure to weight each subset of va...

Data clustering is one of the important data mining methods. It is a process of finding classes of a data set with most similarity in the same class and most dissimilarity between different classes. The well known hard clustering algorithm (K -means) and Fuzzy clustering algorithm (FCM) are mostly based on Euclidean distance measure. In this paper, a comparative study of these algorithms with d...

This paper describes a fuzzy union based approach for automatically evaluating machine generated extract summaries. The proposed method represents every sentence within a machine generated summary as a fuzzy set. Sentences in the reference summary are assigned membership grades in each of these fuzzy sets using cosine distance measure. Finally Fuzzy union (s-norm operation) is used to compute a...