Imprecise Shannon’s Entropy and Multi Attribute Decision Making

Lotfı, Farhad Hosseinzadeh; Fallahnejad, Reza

doi:10.3390/e12010053

Cited by 313 publications

(157 citation statements)

References 11 publications

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“…Finally, the similarity of Information entropy is a method to measure the uncertainty of information that can be used to evaluate its influencing factors and is widely used in various fields (e.g., word alignment, data mining, information theory) related to computer science [35]. Entropy is a well-known method for obtaining the objective weights for a multiple attribute decision making (MADM) problem [23,36], which refers to making preference decisions over the available alternatives that are characterized by multiple attributes. The process of obtaining weights w j as following steps [23]:…”

Section: The Entropy-weighted Multi-attributes Methodsmentioning

confidence: 99%

“…Entropy is a well-known method for obtaining the objective weights for a multiple attribute decision making (MADM) problem [23,36], which refers to making preference decisions over the available alternatives that are characterized by multiple attributes. The process of obtaining weights w j as following steps [23]:…”

Section: The Entropy-weighted Multi-attributes Methodsmentioning

confidence: 99%

“…(1) The names of POIs in VGI have no canonical and authoritative coding standard due to conceptual and semantic ambiguity; traditional text similarity assessment (e.g., Edit Distance) would hamper the quantification of the semantic similarity between names because semantic disambiguation is ignored naturally; (2) Because POIs from multi-sources have diverse taxonomies, current research studies in geospatial data match or fusion rarely resolve the heterogeneity between different classifications due to the lack of an ontology library and domain experts; (3) Because the feature of the property similarity metric always exhibits a non-linear distribution, a new technique to confirm that the appropriate weights must be used to exploit the distribution and reflect the deviations of the attributes simultaneously [22,23].…”

Section: Related Workmentioning

confidence: 99%

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Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Xing

Xia

et al. 2016

Entropy

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Abstract:The crucial problem for integrating geospatial data is finding the corresponding objects (the counterpart) from different sources. Most current studies focus on object matching with individual attributes such as spatial, name, or other attributes, which avoids the difficulty of integrating those attributes, but at the cost of an ineffective matching. In this study, we propose an approach for matching instances by integrating heterogeneous attributes with the allocation of suitable attribute weights via information entropy. First, a normalized similarity formula is developed, which can simplify the calculation of spatial attribute similarity. Second, sound-based and word segmentation-based methods are adopted to eliminate the semantic ambiguity when there is a lack of a normative coding standard in geospatial data to express the name attribute. Third, category mapping is established to address the heterogeneity among different classifications. Finally, to address the non-linear characteristic of attribute similarity, the weights of the attributes are calculated by the entropy of the attributes. Experiments demonstrate that the Entropy-Weighted Approach (EWA) has good performance both in terms of precision and recall for instance matching from different data sets.

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Section: The Entropy-weighted Multi-attributes Methodsmentioning

confidence: 99%

Section: The Entropy-weighted Multi-attributes Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Xing

Xia

et al. 2016

Entropy

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“…According to Saad et al [26], Shannon's entropy concept is 'appropriate for calculating the relative contrast intensities of criteria to represent the average intrinsic information transmitted to the decision maker'. The Shannon's entropy method which was extended by Lotfi & Fallahnejad [27] for imprecise data, especially for interval and fuzzy data case, has found application in several fields of studies including, management, engineering, information sciences, agricultural sciences etc. and has prominently been used in the determination of criteria weight.…”

Section: Fuzzy Shannon's Entropymentioning

confidence: 99%

Application of Intuitionistic Fuzzy Topsis Model for Troubleshooting an Offshore Patrol Boat Engine

Aikhuele

Sorooshian

Ansah

et al. 2017

Polish Maritime Research

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In this paper, an Intuitionistic Fuzzy TOPSIS model which is based on a score function is proposed for detecting the root cause of failure in an Offshore Boat engine, using groups of expert's opinions. The study which has provided an alternative approach for failure mode identification and analysis in machines, addresses the machine component interaction failures which is a limitation in existing methods. The results from the study show that although early detection of failures in engines is quite difficult to identify due to the dependency of their systems from each other. However, with the Intuitionistic Fuzzy TOPSIS model which is based on an improved score function such faults/failuresare easily detected using expert's based opinions.

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“…엔트로피 이론은 불확실성을 나타내는 일반적 척도로서 정보 이론과 교통 모델 등에서 널리 사용되고 있다 (Lotfi and Fallahnejad, 2010;Jafari, 2013). , j = 1,...,n, i = 1,...,n (1)…”

Section: 샤논 엔트로피 분석unclassified

An Approach of Social Vulnerability Assessment to Urban Flood

Lee

Choi

Jun³

2016

Journal of Korean Society of Hazard Mitigation

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In this research, the assessment technique to evaluate the degree of vulnerability to a flood disaster, which reflects the environmental and social characteristics in the light of the urban environment, was addressed. In particular, the road transportation problems, which are affected by inland inundation in urban areas and by the vulnerable groups, such as inhabitants' age and disability, are considered in the evaluation. Consequently, the assessment technique is more focused on the life and management of urban functions than the amount of damage. Vulnerability assessment procedures comprise three stages, i.e., composing the assessment index to reflect the characteristics of the cities and inhabitants, calculating the weight, and evaluating the vulnerability using the TOPSIS technique. The weights were determined using the Delphi method and Entropy analysis, which employed the typical subjective weight assessment technique and objective weight assessment technique, respectively. The target area for the evaluation was the Dorimcheon basin in which a flood had occurred because of the influence of backwater from the nearby streams and the poor rainfall drainage system. The established approach, employed as an urban flood vulnerability assessment technique that is able to consider the characteristics of each region, makes it possible to predict the inherent vulnerable factors in urban regions and to propose the area of priority control when urban floods occur.

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Imprecise Shannon’s Entropy and Multi Attribute Decision Making

Abstract: Finding the appropriate weight for each criterion is one of the main points in

Cited by 313 publications

References 11 publications

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Entropy-Weighted Instance Matching Between Different Sourcing Points of Interest

Application of Intuitionistic Fuzzy Topsis Model for Troubleshooting an Offshore Patrol Boat Engine

An Approach of Social Vulnerability Assessment to Urban Flood

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