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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2018

08.09.2017

Concept Learning Using Vague Concept Lattice

verfasst von: Prem Kumar Singh

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

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Abstract

Recently, the theory of Formal Concept Analysis is extensively studied with bipolar fuzzy setting for adequate analysis of vagueness in fuzzy attributes via a defined sharp boundary. However, many real life data sets contain vague attributes (i.e. beautiful, bald, and tadpole) which cannot be defined through a sharp or restricted boundaries. To process these types of data sets the current paper focused on disparate representation of vagueness in attributes through its evidence to support and reject. To achieve this goal, a method is proposed to generate vague concept lattice with an illustrative example.
Vorheriger Artikel Stochastic Support Vector Machine for Classifying and Regression of Random Variables
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Literatur
1.
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (eds) Ordered sets, NATO advanced study institutes series 83. pp 445–470
2.
Ganter B, Wille R (1999) Formal concept analysis: mathematical foundation. Springer, BerlinCrossRefMATH
3.
Davey BA, Priestley HA (2002) Introduction to lattices and order. Cambridge University Press, CambridgeCrossRefMATH
4.
Burusco Juandeaburre A, Funtes Gonzales R (1994) The study of the \(L\)-fuzzy concept lattice. Math Soft Comput 1(3):209–218
5.
Singh PK (2016) Processing linked formal fuzzy context using non-commutative composition. Inst Integr Omics Appl Biotechnol (IIOAB) J 7(5):21–32
6.
7.
Singh PK, Kumar CA, Li J (2016) Knowledge representation using interval-valued fuzzy concept lattice. Soft Comput 20(4):1485–1502CrossRefMATH
8.
9.
Yao YY (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20MathSciNetCrossRef
10.
11.
Singh PK (2017) Complex vague set based concept lattice. Chaos Solitons Fractals 96:145–153CrossRefMATH
12.
Li J, Huang C, Qi J, Qian Y, Liu W (2017) Three-way cognitive concept learning via multi-granularity. Inf Sci 378:244–263CrossRef
13.
Singh PK (2017) Three-way fuzzy concept lattice representation using neutrosophic set. Int J Mach Learn Cybern 8(1):69–79CrossRef
14.
Pandey LK, Ojha KK, Singh PK, Singh CS, Dwivedi S, Bergey EA (2016) Diatoms image database of India (DIDI): a research tool. Environ Technol Innov 5:148–160CrossRef
15.
Li JH, Mei CL, Lv YJ (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(1):149–165MathSciNetCrossRefMATH
16.
Gao ZK, Cai Q, Yang YX, Dang WD, Zhang SS (2016) Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series. Sci Rep 6:35622. doi:10.​1038/​srep35622 CrossRef
17.
Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS (2017) Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG. Int J Neural Syst 27(4):1750005. doi:10.​1142/​S012906571750005​8 CrossRef
18.
Gao ZK, Fang PC, Ding MS, Jin ND (2015) Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow. Exp Therm Fluid Sci 60:157–164CrossRef
19.
Gao ZK, Yang Y, Zhai L, Jin N, Chen G (2016) A four-sector conductance method for measuring and characterizing low-velocity oil–water two-phase flows. IEEE Trans Instrum Meas 65:1690–1697CrossRef
20.
Dubois D, Prade H (2015) Formal concept analysis from the standpoint of possibility theory. In: Proceedings of ICFCA 2015, LNAI 9113. pp 21–38
21.
22.
Yao YY (2004) Concept lattices in rough set theory. In: Proceedings of 2004 annual meeting of the North American fuzzy information processing society. IEEE Computer Society, Washington, DC, pp 796–801
23.
Bělohlávek R, Vychodil V (2005) What is fuzzy concept lattice. In: Proceedings of CLA Olomuc. Czech Republic, pp 34–45
24.
Singh PK, Kumar CA, Gani A (2016) A comprehensive survey on formal concept analysis, its research trends and applications. Int J Appl Math Comput Sci 26(2):495–516MathSciNetCrossRefMATH
25.
Singh PK, Kumar CA (2014) A note on bipolar fuzzy graph representation of concept lattice. Int J Comput Sci Math 5(4):381–393MathSciNetCrossRef
26.
Djouadi Y, Prade H (2009) Interval-valued fuzzy formal concept analysis. In: Rauch J et al (eds) ISMIS 2009, LNAI 5722. Springer, Berlin, pp 592–601
27.
28.
29.
Akram M, Feng F, Sarwar S, Jun YB (2014) Certain type of vague graphs. UPB Bull Sci Ser 76(1):143–154MathSciNetMATH
30.
Bloch I (2009) Geometry of spatial vague sets based on vague numbers and mathematical morphology. In: Fuzzy logic and applications, lecture notes in computer science, vol 5571. pp 237–245
31.
32.
Chunsheng C, Zhenchun Z, Feng L, Ying Q (2012) Application of vague set in recommender systems. In: Proceedings of 2nd international conference on logistics, informatics and service science (LISS), pp 1353–1359. doi:10.​1007/​978-3-642-32054-5-192
33.
34.
Zeng W, Zhao Y, Gu Y (2016) Similarity measure for vague sets based on implication functions. Knowl Based Syst 94:124–131CrossRef
35.
Zhang Q, Zeng G, Xiao C, Yue Y (2014) A rule conflict resolution method based on Vague set. Soft Comput 18:549–555CrossRef
36.
37.
38.
Ramakrishna N (2009) Vague graphs. Int J Comput Cogn 7:51–58
39.
40.
41.
Macko J (2013) User-friendly fuzzy FCA. In: Lecture notes in computer science, vol 7880. Springer, pp 156–171
42.
Djouadi Y (2011) Extended Galois derivation operators for information retrieval based on fuzzy formal concept lattice. In: Benferhal S, Goant J (eds) SUM 2011, LNAI 6929. Springer, Berlin, pp 346–358
43.
Singh PK, Gani A (2015) Fuzzy concept lattice reduction using Shannon entropy and Huffman coding. J Appl Non-classical Log 25(2):101–119MathSciNetCrossRef
44.
45.
46.
47.
48.
49.
Singh PK, Kumar CA (2012) Interval-valued fuzzy graph representation of concept lattice. In: Proceedings of twelfth international conference on intelligent system design and application 2012. pp 604–609
50.
51.
Rosenfeld A (1975) Fuzzy graphs. In: Zadeh LA, Fu KS, Shimura M (eds) Fuzzy sets and their applications. Academic Press, New York, pp 77–95
52.
Singh PK, Kumar CA (2014) A note on computing the crisp order context of a fuzzy formal context for knowledge reduction. J Inf Process Syst 11(2):184–204
53.
Metadaten
Titel
Concept Learning Using Vague Concept Lattice
verfasst von
Prem Kumar Singh
Publikationsdatum
08.09.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9699-y

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