2,807 research outputs found
Strict Intuitionistic Fuzzy Distance/Similarity Measures Based on Jensen-Shannon Divergence
Being a pair of dual concepts, the normalized distance and similarity
measures are very important tools for decision-making and pattern recognition
under intuitionistic fuzzy sets framework. To be more effective for
decision-making and pattern recognition applications, a good normalized
distance measure should ensure that its dual similarity measure satisfies the
axiomatic definition. In this paper, we first construct some examples to
illustrate that the dual similarity measures of two nonlinear distance measures
introduced in [A distance measure for intuitionistic fuzzy sets and its
application to pattern classification problems, \emph{IEEE Trans. Syst., Man,
Cybern., Syst.}, vol.~51, no.~6, pp. 3980--3992, 2021] and [Intuitionistic
fuzzy sets: spherical representation and distances, \emph{Int. J. Intell.
Syst.}, vol.~24, no.~4, pp. 399--420, 2009] do not meet the axiomatic
definition of intuitionistic fuzzy similarity measure. We show that (1) they
cannot effectively distinguish some intuitionistic fuzzy values (IFVs) with
obvious size relationship; (2) except for the endpoints, there exist infinitely
many pairs of IFVs, where the maximum distance 1 can be achieved under these
two distances; leading to counter-intuitive results. To overcome these
drawbacks, we introduce the concepts of strict intuitionistic fuzzy distance
measure (SIFDisM) and strict intuitionistic fuzzy similarity measure (SIFSimM),
and propose an improved intuitionistic fuzzy distance measure based on
Jensen-Shannon divergence. We prove that (1) it is a SIFDisM; (2) its dual
similarity measure is a SIFSimM; (3) its induced entropy is an intuitionistic
fuzzy entropy. Comparative analysis and numerical examples demonstrate that our
proposed distance measure is completely superior to the existing ones
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866
Temporal fuzzy association rule mining with 2-tuple linguistic representation
This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules
Elite Capture Through Information Distortion: Uniformly Distributed Signal
Common wisdom as well as well-grounded analytical arguments suggest that stronger punishment of deviant behavior meted out by a principal typically prompts the agents to better conform with his objectives. Addressing the specific issue of donor-beneficiary relationships in the context of participatory development programs, we nevertheless show that greater tolerance on the part of donors may, under certain conditions, favor rather than hurt the interests of the poor. Also, greater uncertainty surrounding the donor's knowledge regarding the poor's preference may have the same paradoxical effect. Critical features of our framework are: (i) communities are heterogeneous and dominated by the local elite in dealing with external agencies, (ii) the elite choose the project proposed to the donor strategically, knowing that the latter has a certain amount of tolerance toward elite capture and an imperfect knowledge of the poor's priorities.community-driven development, aid effectiveness, elite capture, corruption, preference targeting.
- …