Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2012

01-09-2012 | Original Article

Visual tracking of the millennium development goals with a fuzzified self-organizing neural network

Author: Peter Sarlin

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2012

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper uses the self-organizing map (SOM), a neural network-based projection and clustering technique, for monitoring the millennium development goals (MDGs). The eight MDGs represent commitments to reduce poverty and hunger, and to tackle ill-health, gender inequality, lack of education, lack of access to clean water and environmental degradation by 2015. This paper presents a SOM model for cross sectional and temporal visual benchmarking of countries and pairs the map with a geospatial dimension by mapping the clustering onto a geographic map. The temporal monitoring is facilitated by fuzzifying the second-level clustering with membership degrees. By creating an MDG index, and associating the SOM model with it, the model enables cross sectional and temporal analysis of the overall MDG progress of countries or regions. Further, the SOM model enables analysis of country-specific as well as regional performance according to a user-specified level of aggregation. The result of this paper is an MDG map for visual tracking and monitoring of the progress of MDG indicators.
previous article On multigranulation rough sets in incomplete information system
next article A novel semisupervised SVM for pixel classification of remote sensing imagery

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Footnotes
1
The HDI, for example, has been criticized for the way its component indices are derived by the raw data (see Noorbakhsh [17]) and the additivity of the aggregation method (see Sagar and Najam [23]).
 
2
For a thorough discussion of the software, see Deboeck [6].
 
Literature
1.
Alkire S, Santos ME (2010) Acute multidimensional poverty: a new index for developing countries. Oxford Poverty and Human Development Initiative, Working Paper 38, University of Oxford
2.
Boehme O, Hardoon D, Manevitz L (2011) Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cybern 2(3):125–134CrossRef
3.
Collan M, Eklund T, Back B (2007) Using the self-organizing map to visualize and explore socio-economic development. EBS Rev 22(1):6–15
4.
Coudouel A, Hentschel JS, Wodon QD (2002) Poverty measurement and analysis. In: Klugman J (ed) A sourcebook for poverty reduction strategies. The International Bank for Reconstruction and Development/The World Bank, Washington, pp 29–69
5.
Cox T, Cox M (2001) Multidimensional scaling. Chapman & Hall/CRC, Boca RatonMATH
6.
Deboeck G (1998) Best practices in data mining using self-organizing maps. In: Deboeck G, Kohonen T (eds) Visual explorations in finance with self-organizing maps. Springer, Berlin, pp 201–229
7.
Eklund T, Back B, Vanharanta H, Visa A (2008) Evaluating a SOM-based financial benchmarking tool. J Emerg Technol Acc 5(1):109–127CrossRef
8.
Graaff AJ, Engelbrecht AP (2011) Clustering data in stationary environments with a local network neighborhood artificial immune system. Int J Mach Learn Cybern. doi:10.​1007/​s13042-011-0041-0
9.
10.
Kaski S (1997) Data exploration using self-organizing maps. Acta Polytechnica Scandinavica, Mathematics, Computing and Management in Engineering Series No. 82., Espoo
11.
Kaski S, Kohonen T (1996) Exploratory data analysis by the self-organizing map: structures of welfare and poverty in the world. In: Proceedings of the 3rd International Conference on Neural Networks in the Capital Markets. World Scientific, London, pp 498–507
12.
Kaski S, Venna J, Kohonen T (2000) Coloring that reveals cluster structures in multivariate data. Aust J Intell Inf Process Syst 6:82–88
13.
14.
15.
16.
Naq AK, Mitra A (2002) Identifying patterns of socio-economic development using self-organizing maps. J Soc Econ Dev 4(1):55–88
17.
Noorbakhsh FA (1998) A modified human development index. World Dev 26:517–528CrossRef
18.
Prados de la Escosura L (2010) Improving human development: a long-run view. CEPR discussion Paper 7982
19.
Prennushi G, Rubio G, Subbarao K (2002) Monitoring and evaluation. In: Klugman J (ed) A sourcebook for poverty reduction strategies. The International Bank for Reconstruction and Development/The World Bank, Washington, pp 105–130
20.
Ravallion M (2010) Mashup indices of development. Policy Research Working Paper 5432, World Bank
21.
Resta M (2009) Early warning systems: an approach via self organizing maps with applications to emergent markets. In: Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks. IOS Press, The Netherlands
22.
Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, CambridgeMATH
23.
Sagar AD, Najam A (1998) The human development index: a critical review. Ecol Econ 25:249–264CrossRef
24.
Sahn DE, Stifel DC (2003) Progress toward the millennium development goals in Africa. World Dev 31(1):23–52CrossRef
25.
Samad T, Harp SA (1992) Self-organization with partial data. Netw Comput Neural Syst 3:205–212CrossRef
26.
Sammon JW (1969) A non-linear mapping for data structure analysis. IEEE Tran Comput 18(5):401–409CrossRef
27.
Sarlin P, Eklund T (2011a) Financial performance analysis of European banks using a fuzzified self-organizing map. In: Proceedings of the 15th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2011), Springer, Kaiserslautern, September 12–14, 2011, pp 185–194
28.
Sarlin P, Eklund T (2011b) Fuzzy clustering of the self-organizing map: some applications on financial time series. In: Proceedings of the 8th International Workshop on Self-Organizing Maps (wSOM’11), Springer, Helsinki, June 13–15, 2011, pp 40–50
29.
Sarlin P, Peltonen T (2011) Mapping the state of financial stability. ECB Working Papers No. 1382
30.
Tong DL, Mintram R (2010) Genetic algorithm-neural network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection. Int J Mach Learn Cybern 1:75–87CrossRef
31.
Tukey JW (1977) Exploratory data analysis. Addison-Wesley, ReadingMATH
32.
Tyler Z, Gopal S (2010) Sub-Saharan Africa at a crossroads—a quantitative analysis of regional development. The Pardee Papers, No. 10, May 2010
33.
34.
UNDP (1993) Human development report. Oxford University Press, New York, also published in various other years
35.
36.
Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11(3):586–600CrossRef
37.
Vesanto J, Sulkava M, Hollmén J (2003) On the decomposition of the self-organizing map distortion measure. In Proceedings of the Workshop on Self-Organizing Maps (wSOM’03), Springer, Hibikino, September 11–14, 2003, pp 11–16
38.
Wang XZ, Dong CR (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567CrossRef
39.
Wang XZ, Zhai JH, Lu SX (2008) Induction of multiple fuzzy decision trees based on rough set technique. Inf Sci 178(16):3188–3202MathSciNetMATHCrossRef
40.
Wang XZ, Dong CR, Fan TG (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587CrossRef
41.
Ward J (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244CrossRef
Metadata
Title
Visual tracking of the millennium development goals with a fuzzified self-organizing neural network
Author
Peter Sarlin
Publication date
01-09-2012
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2012
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0057-5

Other articles of this Issue 3/2012

International Journal of Machine Learning and Cybernetics 3/2012 Go to the issue

Original Article

Improving pattern discovery and visualisation with self-adaptive neural networks through data transformations

Original Article

On multigranulation rough sets in incomplete information system

Original Article

Classification rule discovery using learning automata

Original Article

A vision-based intelligent inspector for wine production

Original Article

A novel semisupervised SVM for pixel classification of remote sensing imagery

Original Article

Structural vibration suppression by using neural classifier with genetic algorithm