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Granular Computing
Erschienen in: Granular Computing 3/2017

18.10.2016 | Original Paper

Granular computing: from granularity optimization to multi-granularity joint problem solving

verfasst von: Guoyin Wang, Jie Yang, Ji Xu

Erschienen in: Granular Computing | Ausgabe 3/2017

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Abstract

Human beings solve problems in different granularity worlds and shift from one granularity world to another quickly. It reflects human beings’ intelligence in problem solving to some extent. In the era of big data, some new problems are emerging in real life. For example, traditional big data processing models always compute from raw data, failing to consider the granularity feature of human. Thus, they are hard to solve the 3 V characteristics of big data. Granular computing (GrC) combines the multi-granularity thinking pattern of human intelligence with problem solving mode to deal with big data. Based on the related notions and characteristics of GrC, this paper reviews the previous studies of GrC in three progressive levels: granularity optimization, granularity conversion and multi-granularity joint problem solving. Then we proposed the diagram for relationship among three basic modes of GrC. Furthermore, the feasibility of GrC for big data processing is analyzed. Some research prospects of granular computing are given.
Nächster Artikel Concepts of solutions of uncertain equations with intervals, probabilities and fuzzy sets for applied tasks

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Literatur
Andrew MA, Erik B (2012) Big data: the management revolution. Harv Bus Rev 90(10):60–6, 68, 128
Antonelli M, Ducange P, Lazzerini B et al (2016) Multi-objective evolutionary design of granular rule-based classifiers. Granul Comput 1(1):37–58CrossRef
Bazan JG (2008) Hierarchical classifiers for complex spatio–temporal concepts. Transactions on rough sets IX. Springer, Berlin, pp 474–750
Bengio Y, Guyon G, Dror V et al (2011) Deep learning of representations for unsupervised and transfer learning. Workshop Unsuperv Transf Learn 7:17–37
Bianchi FM, Livi L, Rizzi A et al (2014) A granular computing approach to the design of optimized graph classification systems. Soft Comput 18(2):393–412CrossRef
Cao Z, Yin Q, Tang X et al (2010) Face recognition with learning-based descriptor. IEEE Conf Comput Vis Pattern Recogn 6:2707–2714
Chen CLP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275(11):314–347CrossRef
Chen H, Li T, Ruan D et al (2011) A rough-set based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans Knowl Data Eng 25(99):1
Ciucci D (2016) Orthopairs and granular computing. Granul Comput 1(3):1–12CrossRef
Clifford L (2008) Big data: how do your data grow? Nature 455(7209):28–29CrossRef
Collins JA, Greer JE, Huang SX (1996) Adaptive assessment using granularity hierarchies and bayesian nets intelligent tutoring systems. Springer, Berlin, pp 569–577
Du Z (2013) Granularities and inconsistencies in big data analysis. Int J Software Eng Knowl Eng 23(6):887–893CrossRef
Dubois D, Prade H (2016) Bridging gaps between several forms of granular computing. Granular Comput 1(2):115–126CrossRef
Erhan D, Bengio Y, Courvulle A et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11(3):625–660MathSciNetMATH
Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202CrossRefMATH
Gantz J, Reinsel D (2011) Extracting value from chaos, Idcemc2 Report
Han K, Yu D, Tashev I (2014) Speech emotion recognition using deep neural network and extreme learning machine. In: Interspeech, pp 223–227
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154CrossRef
Jang RJS (1993) ANFIS: adaptive-network-based fuzzy inference systems. Inst Electr Electron Eng Inc 23(3):665–685
Kreinovich V (2016) Solving equations (and systems of equations) under uncertainty: how different practical problems lead to different mathematical and computational formulations. Granul Comput 1(3):1–9MathSciNetCrossRef
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):2012
Labrinidis A, Jagadish HV (2012) Challenges and opportunities with big data. Proc VLDB Endow 5(12):2032–2033CrossRef
Li DY, Meng HJ, Shi XM (1995) Membership clouds and membership cloud generators. J Comput Res Dev 32(6):15–20
Li DY, Liu CY, Gan WY (2009) A new cognitive model: cloud model. Int J Intell Syst 24:357–375CrossRefMATH
Liang JY, Wang F, Dang CY et al (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294–308CrossRef
Lin Y, Li J, Lin P et al (2014a) Feature selection via neighborhood multi-granulation fusion. Knowl Based Syst 67(3):162–168CrossRef
Lin Y, Li J, Lin M et al (2014b) A new nearest neighbor classifier via fusing neighborhood information. Neurocomputing 143(16):164–169CrossRef
Lingras P, Haider F, Triff M (2016) Granular meta-clustering based on hierarchical, network, and temporal connections. Granul Comput 1(1):71–92CrossRef
Liu YC, Li DY, He W et al (2013) Granular computing based on Gaussian cloud transformation. Fundam Inf 127(14):385–398
Liu HB, Liu CH, Wu CG (2014) Granular computing classification algorithms based on distance measures between granules from the view of set. Comput Intell Neurosci 32(2):150–159
Liu JJ, Wang HX, Wang DS et al (2015) Parallelizing convolutional neural networks on intel many integrated core. architecture architecture of computing systems—ARCS 2015. Springer, Berlin, pp 71–82
Livi L, Sadeghian A (2016) Granular computing, computational intelligence, and the analysis of non-geometric input spaces. Granul Comput 1(1):13–30CrossRef
Loia V, Aniello GD, Gaeta A, Orciuoli F (2016) Enforcing situation awareness with granular computing: a systematic overview and new perspectives. Granular Comput 1(2):127–143CrossRef
Luo P, Wang X, Tang X (2012) Hierarchical face parsing via deep learning. Computer vision and pattern recognition (CVPR), 2012 IEEE Conference on. IEEE:2480–2487
Ma HY, Wang GY, Zhang QH (2012) Multi-granularity color image segmentation based on cloud model. Comput Eng 38(20):184–187
Maciel Leandro, Ballini Rosangela, Gomide Fernando (2016) Evolving granular analytics for interval time series forecasting[J]. Granul Comput 1(4):1–12CrossRef
Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1(1):59–69CrossRef
Mesnil G, Rifai S, Bordes A et al (2015) Unsupervised learning of semantics of object detections for scene categorization. Pattern recognition applications and methods, vol 318. Springer, Berlin, pp 209–224
Min F, Xu J (2016) Semi-greedy heuristics for feature selection with test cost constraints. Granul Comput 1(3):1–13CrossRef
Pai TY, Chang MB, Chen SW (2015) Information granularity, big data, and computational intelligence. In: Pedrycz W, Shyi-Ming C (eds) Studies in big data, vol 8, pp 427–441
Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, DordrechtMATH
Pedrycz W (1998) Shadowed sets: representing and processing fuzzy sets. IEEE Trans Syst Man Cybern Part B Cybern A Publ IEEE Syst Man Cybern Soc 28(1):103–109MathSciNetCrossRef
Pedrycz W (2011) The principle of justifiable granularity and an optimization of information granularity allocation as fundamentals of granular computing. J Inf Process Syst 7(7):397–412CrossRef
Pedrycz W (2013) Granular computing: analysis and design of intelligent systems. CRC Press, Boca RatonCrossRef
Pedrycz W, Chen SM (2011) Granular computing and intelligent systems: design with information granules of higher order and higher type. Springer, BerlinCrossRef
Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13(10):4209–4218CrossRef
Peters G, Weber R (2016) DCC: a framework for dynamic granular clustering. Granul Comput 1(1):1–11CrossRef
Rodriguez A, Laio A (2014) Machine learning. Clustering by fast search and find of density peaks. Science 344(6191):1492–1496CrossRef
Ryjov A (2015) Towards an optimal task-driven information granulation. information granularity, big data, and computational intelligence. Springer, Berlin, pp 191–208
Salakhutdinov Ruslan, Hinton Geoffrey (2009) Deep Boltzmann machines. J Mach Learn Res 24(5):448–455MATH
Schifanella R, Redi M, Aiello L (2015) An image is worth more than a thousand favorites: surfacing the hidden beauty of Flickr pictures. arXiv:​1505.​03358 (preprint)
Skowron A, Wasilewski P (2011) Information systems in modeling interactive computations on granules. Theoret Comput Sci 412:5939–5959MathSciNetCrossRefMATH
Skowron A, Jankowski A, Swiniarski RW (2015) Foundations of rough sets. In: Kacprzyk J, Pedrycz W (eds) Springer handbook of computational intelligence. Springer, Berlin, pp 331–348CrossRef
Skowron A, Jankowski A, Dutta S (2016b) Toward problem solving support based on big data and domain knowledge: interactive granular computing and adaptive judgement. Big data analysis: new algorithms for a new society. Springer, Berlin, pp 424–430
Slezak D, Kowalski M (2013) Intelligent granulation of machine-generated data. IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 joint. IEEE, pp 68–73
Srivastava N, Salakhutdinov R (2014) Multimodal learning with deep boltzmann machines. J Mach Learn Res 15(8):1967–2006MathSciNetMATH
Sun Y, Wang X, Tang X (2014). Deep learning face representation by joint identification-verification. In: International Conference on Neural Information Processing Systems. MIT Press, Cambridge, pp 1988–1996
Synak P, Slezak D (2014) Complexity aspects of multi-machine aggregations in a rough-granular computation framework. Granular computing (GrC), 2014 IEEE international conference on. IEEE, pp 275–280
Tekiner F, Keane JA (2013) big data framework. systems, man, and cybernetics (SMC), 2013 IEEE international conference on. IEEE, pp 1494–1499
Torralba A, Fergus R, Weiss Y (2008) Small codes and large image databases for recognition. 2008 IEEE conference on computer vision and pattern recognition. IEEE:1–8
Wang G, Shi H (1998) TMLNN: triple-valued or multiple-valued logic neural network. IEEE Trans Neural Netw 9(6):1099–1117CrossRef
Wang XG, Tang X (2004) A unified framework for subspace face recognition. IEEE Trans Pattern Anal Mach Intell 26(9):1222–1228CrossRef
Wang GY, Zhang QH et al (2008) Uncertainty of rough sets in different knowledge granularities. Chin J Comput 31(9):1588–1598MathSciNetCrossRef
Wang LW, Zhang XJ, Zhang L (2014) Cluster size analysis based on fuzzy compatibility relation. J Syst 26(7):321–329
Wang Guoyin, Ji Xu, Zhang Qinghua et al (2015) Multi-granularity intelligent information processing[J]. J Stroke Cerebrovasc Dis 2(1):S4–S5
Wilke G, Portmann E (2016) Granular computing as a basis of human–data interaction: a cognitive cities use case. Granul Comput 1(3):1–17CrossRef
Wu XD, Zhu XQ, Wu GQ et al (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107CrossRef
Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35CrossRef
Xu J, Wang GY, Yu H (2015) Review of big data processing based on granular computing. Chin J Comput 8:1497–1517MathSciNet
Yao YY (2000) Granular computing: basic issues and possible solutions. In: Proceedings of the 5th joint conference on information sciences, vol I. Atlantic, NJ: Association for Intelligent Machinery, pp 186–189
Yao YY (2011) Artificial intelligence perspectives on granular computing. Granular computing and intelligent systems. Springer, Berlin, pp 17–34CrossRef
Yao Y (2016) A triarchic theory of granular computing. Granul Comput 1(2):1–13CrossRef
Yao YY, Luo J (2011) Top-down progressive computing. In: Proceedings of the 6th international conference on rough sets and knowledge technology. Springer, Berlin, pp 734–742
Yao YY, Zhong N (1999) Potential applications of granular computing in knowledge discovery and data mining. In: Proceedings of world multiconference on systemics, cybernetics and informatics, vol 5
Yao J, Vasilakos A, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43:1977–1989CrossRef
Zadeh LA (1979) Fuzzy sets and information granulation. Advances in fuzzy set theory and applications. North-Holland Publishing, Amsterdam
Zadeh LA (1997) Toward a theory of fuzzy information Granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127MathSciNetCrossRefMATH
Zhang B, Zhang L (1992) Theory and applications of problem solving. Elsevier Science Inc, OxfordMATH
Zhang L, Zhang B (2003) The quotient space theory of problem solving. Fundam Inf 59(2):11–15MathSciNetMATH
Zhang QH, Wang GY, Liu XQ (2008) School of Information Science and Technology, University S J, Chengdu. Hierarchical structure analysis of fuzzy quotient space. Pattern Recogn Artif Intell
Zhang QH, Guo YL, Xue YB (2015) Multi-granularity search algorithm based on probability statistics. Pattern Recog Artif Intell 28(5):422–427
Zheng S, Jayasumana S, Romera-Paredes B et al (2015) Conditional random fields as recurrent neural networks. arXiv:​1502.​03240 (preprint)
Zhong N, Yau SS, Ma J et al (2015) Brain informatics-based big data and the wisdom web of things. IEEE Intell Syst 30(5):2–7CrossRef
Zhou B, Garcia AL, Xiao J et al (2015) Learning deep features for scene recognition using places database. Adv Neural Inf Process Syst 1:487–495
Metadaten
Titel
Granular computing: from granularity optimization to multi-granularity joint problem solving
verfasst von
Guoyin Wang
Jie Yang
Ji Xu
Publikationsdatum
18.10.2016
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 3/2017
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-016-0032-3

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