Human centricity and information granularity in the agenda of theories and applications of soft computing

doi:10.1016/j.asoc.2014.04.040

Applied Soft Computing

Volume 27, February 2015, Pages 610-613

https://doi.org/10.1016/j.asoc.2014.04.040 Get rights and content

Abstract

Soft computing is an interdisciplinary area that focuses on the design of intelligent systems to process uncertain, imprecise and incomplete information. It mainly builds on fuzzy sets theory, fuzzy logic, neural computing, optimization, evolutionary algorithms, and approximate reasoning et al. Information granularity is in general regarded as a crucial design asset, which helps establish a better rapport of the resulting granular model with the system under modeling. Human centricity is an inherent property of people's view on a system, a process, a machine or a model. Information granularity can be used to reflect people's level of uncertainty and this makes its pivotal role in soft computing. Indeed, the concept of information granularity facilitates the development of theory and application of soft computing immensely. A number of papers pertaining to some recent advances in theoretical development and practical application of information granularity in soft computing are highlighted in this special issue. The main objective of this study is to collect as many as possible researches on human centricity and information granularity in the agenda of theories and applications of soft computing, review the main idea of these literatures, compare the advantages and disadvantages of their methods and try to find the relationships and relevance of these theories and applications.

Introduction

The aim of this special issue is to showcase a small fraction of recent advances of information granularity in assist of development of soft computing. Information granularity can be viewed as an essential asset [3], [4], [30], [31], [37], which offers a tangible level of flexibility to a system (process). Soft computing paradigms [1], [2], in general, aim to produce computing systems/machines that exhibit some useful properties, e.g. making inference with vague and/or ambiguous information, learning from noisy and/or incomplete data, adapting to changing environments, and reasoning with uncertainties. These properties are important for the systems/machines to be useful in assisting humans in our daily activities. It is by no mean exhaustive as this is a fast-moving area in which new techniques and applications related emerge almost every day. Since the first studies on information granulation proposed by Zadeh [36] in 1970s, there have been lots of researchers concentrating on its development. Some books are written to introduce and explain fundamental concepts on human-centric information processing [31], [32], [37], [38], [39]. In this paper, we analyze the literature from two aspects: theories and application of soft computing. The theories development mainly includes a principle of justifiable granularity, allocation of information granularity, granular models, granular neural networks, granular fuzzy models, granular prototypes, measures of granularity of partitions and so on. Many of these theories are applied in different fields, such as medicine, system modeling, system identification, decision making, fuzzy modeling, pattern recognition, formal concept analysis, Web application and food engineering. A summary of each paper is as follows.

Section snippets

Theory development

Within the field of soft computing, some researchers concentrate on the fundamental theories of information granularity. As we know, soft computing builds on fuzzy sets theory, fuzzy logic, neural computing, optimization, evolutionary algorithms, approximate reasoning et al. and is focused on the design of intelligent systems to process uncertain, imprecise and incomplete information. The granularity of information is an inherent manifestation of the diversity of results provided by sources of

Application development

In this paper, we discuss applications of information granularity in different fields, such as medicine, system modeling, system identification, decision making, fuzzy modeling, pattern recognition, formal concept analysis, web application and food engineering.

The third paper [7] is an application of optimal allocation of information granularity in which a collection of electrocardiogram signals are represented by a certain information granule. The granular format of the representative of the

Conclusion

Information granules and their computing, which give rise to the framework of Granular Computing, deliver interesting opportunities to endow processing with an important facet of human-centricity. Human centricity is an inherent property of people's view on a system, a process, a machine or a model. Thus they become important in the agenda of theories and application of soft computing. In this paper, we collect as many as possible related papers and elaborate the main contributions of each

Acknowledgements

Support from the National Natural Science Foundation of China (NSFC) 61305100 and Youth Science Foundation of Communication University of China 3132014XNG1436 are gratefully appreciated.

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Cited by (16)

An overview of granular computing in decision-making: Extensions, applications, and challenges
2023, Information Fusion
The management of uncertainty in decision-making problems remains a very challenging and timely research issue despite many proposals. An interesting topic in this area in recent years has been Granular Computing. In this paper, we present a coherent framework for various models of information granules, using hierarchies of information for dealing with knowledge acquired and processed at different levels of abstraction. Granular Computing, a paradigm for handling higher types of uncertainty in decision analysis, is considered a new conceptual and algorithmic asset of decision-making studies and, in particular, data-driven decision-making. Most relevant studies have focused on categories of information granules, namely intervals, fuzzy sets, rough sets, and shadowed sets. To better understand this promising area, this study provides a comprehensive overview of Granular Computing for decision-making through literature analysis, enhancing a variety of extensions, applications, and challenges.
Granular models as networks of associations of information granules: A development scheme via augmented principle of justifiable granularity
2022, Applied Soft Computing
Citation Excerpt :
The formation of information granules is a key factor for a granular model, as information granules could summarize information at different granular level (Type-1, Type-2) [14]. The basic concept of information granules can be formulated by some existing fuzzy modeling methods and fuzzy models, thus resulting in the appearance of granular fuzzy models [33]. Compared with genetic fuzzy models with numerical output values, granular fuzzy models produce information granules using a certain formalism and extend outputs in a feasible collection, which makes granular fuzzy models in better rapport with experimental data.
This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approach consists of two stages. First, an augmented principle of justifiable granularity is proposed and applied to construct information granules in an input space. This principle constructs information granules not only through establishing a sound balance between two criteria, i.e., coverage and specificity, but also by optimizing those information granules on the basis of their homogeneity assessed with respect to data localized in output space. At the second stage, we propose an inference scheme by analyzing a location of an input datum in relation with the already formed information granules in an input space. The computed relation can be quantified as membership grades, thus yielding aggregation results involving information granules in an output space. The performance of the proposed granular model is supported by the mechanisms of granular computing and the principle of justifiable granularity. Experimental studies concerning synthetic and publicly available data are performed and some comparative analysis involving rule-based models is given.
A semantic facial expression intensity descriptor based on information granules
2020, Information Sciences
Citation Excerpt :
Here information granules are viewed as abstract, semantically meaningful constructs that bring together elements of some resemblance [32]. As an efficient data descriptor, information granules play an important role in the discovery, quantification, and characterization of data [1,22,31,40,50]. Information granules can characterize the vital structure of data, their topology, and essential relationships, and are widely used in fuzzy modeling [37,42], classification [10], controller design [5,6], and image processing [14,33].
This paper investigates a granular description for facial expression intensity in which characterization of motion-based expression features is presented by a collection of information granules. First, the motion-based features are extracted based on the facial landmarks to encode the expression intensity. Secondly, the semantic concepts are then generated by involving various mechanisms of fuzzy clustering based on the motion-based features. The resulting clusters can be viewed as numeric prototypes of the descriptors. The information granules are formed around the prototypes according to the principle of justifiable granularity, which is the fundamental idea of Granular Computing. The proposed granular descriptor for expression intensity is tested on the BU-3DFE database, and the experimental results illustrate that the proposed descriptors based on information granules not only can characterize the semantics of facial changes caused by expressions, but also can facilitate the semantic estimation of facial expression intensity.
Facial semantic descriptors based on information granules
2019, Information Sciences
Citation Excerpt :
Information granules are regarded as an efficacy data descriptors, which can characterize the key structure of data, their topology and essential relationships [1,2]. And they have been playing a visible role in the discovery, quantification and characterization of data, see e.g. [3–8]. The formation of information granules is based on Granular Computing [9–12], which is focused on developing, characterizing and processing information granules.
In this paper, we investigate a granular data description for facial components in which a characterization of facial components is presented by a collection of information granules. Firstly, the facial landmark detector is utilized to extract facial components automatically. Secondly, semantic concepts are formed by involving various mechanisms of fuzzy clustering based on these detected landmarks. A collection of numeric prototypes can be sought as a blueprint of the descriptors. Consequently, the information granules are being formed around the prototypes that are engaged by the fundamental ideas of Granular Computing, especially the principle of justifiable granularity. Multiple experiments on Multi-PIE facial database illustrate the proposed facial semantic descriptors based on information granules not only can characterize the key semantics of facial components of data, but also can improve the semantic classification performance in comparison with human perception.
Development of granular models through the design of a granular output spaces
2017, Knowledge-Based Systems
Citation Excerpt :
Granular modeling is about generalizing, processing, and optimizing numeric models to a granular form by augmenting some functional aspects of the numeric models and bringing them as information granules. With the advantages of Granular Computing and ensuing information granules, granular models not only are processed in a certain way so that they align with a way humans perceive the world, organize domain knowledge, and carry out processing, usually in an approximate, non-numeric way, but also delivers a higher level of tolerance to errors and becomes more in rapport with the real-world phenomenon (system), also refer to [9–12,24]. In the existing literatures, whatever the mechanisms are engaged to achieve optimal allocation of information granularity, the performance evaluation function (objective function) of granular models is quite complex, as it includes two conflicting criteria, coverage and specificity, and a condition of predetermined level of information granularity (the details will be provided later).
It becomes apparent that there are no ideal numeric models. Bringing a concept of information granularity to the original numeric model makes it well aligned with the experimental data and helps deliver a better insight into the credibility of the results provided by the model. Information granularity is regarded as a crucial design asset being optimally allocated across the numeric parameters of the originally constructed model. The underlying objective of this study is to propose a concept of a granular output space and develop an optimization process of allocation of information granularity across this space. The optimization is carried out by optimizing output information granules produced by the granular model by considering a product of the essential criteria describing information granules, namely specificity and coverage. The detailed optimization procedure involving Particle Swarm Optimization (PSO) is presented. We stress a generality of the approach that cuts across a variety of classes of models. A collection of experimental studies involving interval information granules is reported demonstrating the main features of the proposed approach.
Data reconstruction with information granules: An augmented method of fuzzy clustering
2017, Applied Soft Computing Journal
Citation Excerpt :
Information granularity has been playing a visible role in the discovery, quantification and characterization of data, see e.g. [1–6].
Information granules form an abstract and efficient characterization of large volumes of numeric data. Fuzzy clustering is a commonly encountered information granulation approach. A reconstruction (degranulation) is about decoding information granules into numeric data. In this study, to enhance quality of reconstruction, we augment the generic data reconstruction approach by introducing a transformation mapping of the originally produced partition matrix and setting up an adjustment mechanism modifying a localization of the prototypes. We engage several population-based search algorithms to optimize interaction matrices and prototypes. A series of experimental results dealing with both synthetic and publicly available data sets are reported to show the enhancement of the data reconstruction performance provided by the proposed method.

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Human centricity and information granularity in the agenda of theories and applications of soft computing

Abstract

Introduction

Section snippets

Theory development

Application development

Conclusion

Acknowledgements

Expert Syst. Appl.

Sci. Iran.

Appl. Soft Comput.

Eur. J. Oper. Res.

Artif. Intell. Med.

Appl. Soft Comput.

Inf. Sci.

Inf. Sci.

Expert Syst. Appl.

Knowl. Based Syst.

Appl. Soft Comput.

Pattern Recognit.

Fuzzy Sets Syst.

Neurocomputing

Inf. Sci.

Int. J. Approx. Reason.

J. Food Eng.

Eur. J. Oper. Res.

Soft computing in analytics: handling imprecision and uncertainty in strategic decisions

Fuzzy Econ. Rev.