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

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):37–58

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):393–412

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):314–347

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):1–12

Clifford L (2008) Big data: how do your data grow? Nature 455(7209):28–29

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):887–893

Dubois D, Prade H (2016) Bridging gaps between several forms of granular computing. Granular Comput 1(2):115–126

Erhan D, Bengio Y, Courvulle A et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11(3):625–660

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):193–202

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

Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154

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):1–9

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):2032–2033

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 24:357–375

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):294–308

Lin Y, Li J, Lin P et al (2014a) Feature selection via neighborhood multi-granulation fusion. Knowl Based Syst 67(3):162–168

Lin Y, Li J, Lin M et al (2014b) A new nearest neighbor classifier via fusing neighborhood information. Neurocomputing 143(16):164–169

Lingras P, Haider F, Triff M (2016) Granular meta-clustering based on hierarchical, network, and temporal connections. Granul Comput 1(1):71–92

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):13–30

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):127–143

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):1–12

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):59–69

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):1–13

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 (1982) Rough sets. Int J Comput Inform Sci 11(5):341–356

Pawlak Z (1992) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht

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):103–109

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):397–412

Pedrycz W (2013) Granular computing: analysis and design of intelligent systems. CRC Press, Boca Raton

Pedrycz W, Chen SM (2011) Granular computing and intelligent systems: design with information granules of higher order and higher type. Springer, Berlin

Pedrycz W, Homenda W (2013) Building the fundamentals of granular computing: a principle of justifiable granularity. Appl Soft Comput 13(10):4209–4218

Peters G, Weber R (2016) DCC: a framework for dynamic granular clustering. Granul Comput 1(1):1–11

Ranzato M, Hinton G, Lecun Y (2015) Guest editorial: deep learning. Int J Comput Vis 113(1):1–2

Rodriguez A, Laio A (2014) Machine learning. Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

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):448–455

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)

Simpson AJR (2015) On-the-fly learning in a perpetual learning machine. arXiv:1509.00913 (preprint)

Skowron A, Wasilewski P (2011) Information systems in modeling interactive computations on granules. Theoret Comput Sci 412:5939–5959

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 331–348

Skowron A, Jankowski A, Dutta S (2016a) Interactive granular computing. Granul Comput 1(2):1–19

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):1967–2006

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):1099–1117

Wang XG, Tang X (2004) A unified framework for subspace face recognition. IEEE Trans Pattern Anal Mach Intell 26(9):1222–1228

Wang GY, Zhang QH et al (2008) Uncertainty of rough sets in different knowledge granularities. Chin J Comput 31(9):1588–1598

Wang GY, Yao YY, Yu H (2009) A survey on rough set theory and applications. Chin J Comput 32(7):1229–1246

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):1–17

Wu XD, Zhu XQ, Wu GQ et al (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

Xu Z, Wang H (2016) Managing multi-granularity linguistic information in qualitative group decision making: an overview. Granul Comput 1(1):21–35

Xu J, Wang GY, Yu H (2015) Review of big data processing based on granular computing. Chin J Comput 8:1497–1517

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 17–34

Yao Y (2016) A triarchic theory of granular computing. Granul Comput 1(2):1–13

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 43:1977–1989

Zadeh LA (1965) Fuzzy sets. Inf Control 8(65):338–353

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):111–127

Zhang B, Zhang L (1992) Theory and applications of problem solving. Elsevier Science Inc, Oxford

Zhang L, Zhang B (2003) The quotient space theory of problem solving. Fundam Inf 59(2):11–15

Zhang L, Zhang B (2005) Fuzzy reasoning model under quotient space structure. Inf Sci 173(4):353–364

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):2–7

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