Granular computing as a basis of human–data interaction: a cognitive cities use case

Wilke, Gwendolin; Portmann, Edy

doi:10.1007/s41066-016-0015-4

Cited by 91 publications

(24 citation statements)

References 55 publications

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“…In real applications, the granular computing theory has been popularly used for advancing other research areas, such as computational intelligence (Dubois and Prade 2016;Kreinovich 2016;Yao 2005b;Livi and Sadeghian 2016), artificial intelligence (Wilke and Portmann 2016;Yao 2005b;Skowron et al 2016), and machine learning (Min and Xu 2016;Peters and Weber 2016;Liu and Cocea 2017b;Antonelli et al 2016). In addition, ensemble learning is an area that has a strong link with granular computing.…”

Section: Granular Computingmentioning

confidence: 99%

Fuzzy information granulation towards interpretable sentiment analysis

Liu

Cocea

2017

Granul. Comput.

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Section: Granular Computingmentioning

confidence: 99%

Fuzzy information granulation towards interpretable sentiment analysis

Liu

Cocea

2017

Granul. Comput.

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“…In addition, granular computing [25] is a flexible and feasible tool for decision A C C E P T E D M A N U S C R I P T 29 makers to address the challenges of characterizing their preferences in an uncertain context. Recently, a number of achievements [2,23,28,29,36,42,44] have been made in this area which contribute to further studies of uncertain decision making. All these areas are worth exploring in future research to solve uncertain MADM problems.…”

Section: Comparative Analysismentioning

confidence: 99%

Analysis of fuzzy Hamacher aggregation functions for uncertain multiple attribute decision making

Tang

et al. 2017

Information Sciences

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As generalizations of algebraic and Einstein t-norms and t-conorms, Hamacher t-norm and t-conorm have been widely applied in fuzzy multiple attribute decision making (MADM) to combine assessments on each attribute, which are generally expressed by Atanassov"s intuitionistic fuzzy (AIF) numbers, interval-valued intuitionistic fuzzy (IVIF) numbers, hesitant fuzzy (HF) elements, and dual hesitant fuzzy (DHF) elements. Due to the fact that AIF numbers and HF elements are special cases of IVIF numbers and DHF elements, respectively, two propositions can be established from analyzing numerical examples and real cases concerning MADM with IVIF and DHF assessments in the literature: (1) the monotonicity of alternative scores derived from Hamacher arithmetic and geometric aggregation operators with respect to the parameter r in Hamacher t-norm and t-conorm; and (2) the relationship between alternative scores generated by Hamacher arithmetic and geometric aggregation operators, given the same r. Here, we provide the theoretical proof of these two propositions in the context of MADM with IVIF and DHF assessments.With the theoretical support of these propositions, the meaning of r in MADM is explained, and a new method is proposed to compare alternatives in MADM with consideration of all possible values of r. Two numerical examples are solved by the proposed method and the other two existing methods to demonstrate the applicability and validity of the proposed method and highlight its advantages.

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“…There are also some similar granular computing models developed to simulate and implement human granular thinking and problem solving, such as, interactive granular computing (Skowron et al 2016;Wilke and Portmann 2016), granular neural network (Song and Wang 2016), granular clustering (Peters and Weber 2016;Yu et al 2016;Xu et al 2016), etc. 4 Data-driven granular cognitive computing:…”

Section: Cognitive Computing: Brain/mind Inspired Computingmentioning

confidence: 99%

DGCC: data-driven granular cognitive computing

Wang

2017

Granul. Comput.

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2016 is the 60 anniversary of the birth of artificial intelligence (AI). In the past 60 years, many AI theoretical models have been proposed and great achievements have been accomplished. Most intelligent computing models are inspired by various human/ natural/social intelligence mechanisms. Three main schools of artificial intelligence are formed, that is, symbolism, connectionism and behaviorism. Artificial intelligence is intelligence exhibited by machines. It is applied when a machine mimics cognitive functions that humans associate with other human minds. Achievements of cognitive science could give much inspiration to AI. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind's capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. Cognitive computing is the third and most transformational phase in computing's evolution, after the two distinct eras of computing-the tabulating era and the programming era. Inspired by human's granularity thinking, problem solving mechanism and the cognition law of ''global precedence'', a new powerful cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this paper. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human's cognition mechanism of ''global precedence'' which is a cognition process of ''from coarser to finer'' and the information processing mechanism of machine learning systems which is ''from finer to coarser'', in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper. The HD 3 characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

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Granular computing as a basis of human–data interaction: a cognitive cities use case

Cited by 91 publications

References 55 publications

Fuzzy information granulation towards interpretable sentiment analysis

Fuzzy information granulation towards interpretable sentiment analysis

Analysis of fuzzy Hamacher aggregation functions for uncertain multiple attribute decision making

DGCC: data-driven granular cognitive computing

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