Neural Computing and Applications

, Volume 29, Issue 7, pp 375–388 | Cite as

FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens

  • Ilias Bougoudis
  • Konstantinos Demertzis
  • Lazaros Iliadis
  • Vardis-Dimitris Anezakis
  • Antonios Papaleonidas
S.I. : EANN 2016


Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.


Air quality Air pollution Fuzzy logic Semi-supervised learning Semi-supervised clustering Semi-supervised classification 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Education Research Centre of Greece. Accessed 1 Feb 2017
  2. 2.
    Bougoudis I, Iliadis L, Papaleonidas A (2014) Fuzzy inference ANN ensembles for air pollutants modeling in a major urban area: the case of Athens. Eng Appl Neural Netw Commun Comput Inf Sci 459:1–14. doi: 10.1007/978-3-319-11071-4_1 Google Scholar
  3. 3.
    Iliadis L, Bougoudis L, Spartalis S (2014) Comparison of self organizing maps clustering with supervised classification for air pollution data sets. Proc AIAI 436:424–435. doi: 10.1007/978-3-662-44654-6_42 Google Scholar
  4. 4.
    Bougoudis I, Demertzis K, Iliadis L (2016) Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning. Integr Comput Aided Eng 23(2):115–127. doi: 10.3233/ICA-150505 CrossRefGoogle Scholar
  5. 5.
    Bougoudis I, Demertzis K, Iliadis L, Anezakis VD, Papaleonidas A (2016) Semi-supervised hybrid modeling of atmospheric pollution in urban centers. Commun Comput Inf Sci 629:51–63Google Scholar
  6. 6.
    Bougoudis I, Demertzis K, Iliadis L (2016) HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. EANN Neural Comput Appl 27:1191–1206. doi: 10.1007/s00521-015-1927-7 CrossRefGoogle Scholar
  7. 7.
    Krithara A, Amini MR, Renders JM, Goutte C (2008) Semi-supervised document classification with a mislabeling error model. In: 30th European conference on IR research, ECIR 2008, advances in information retrieval, lecture notes in computer science, 4956:370–381. doi: 10.1007/978-3-540-78646-7_34
  8. 8.
    Ashfaq RAR, Wang XZ, Huang JZ, Abbas H, He YL (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484–497. doi: 10.1016/j.ins.2016.04.019 CrossRefGoogle Scholar
  9. 9.
    Yan Y, Chen L (2011) Label-based semi-supervised fuzzy co-clustering for document categoraization. In: 8th international conference on information, communications and signal processing, (ICICS) pp 1–5. doi: 10.1109/ICICS.2011.6173605
  10. 10.
    Zheng A, Luo L (2012) A semi-supervised fuzzy SVM clustering framework. Recent advances in computer science and information engineering, lecture notes in electrical engineering, 1:525–530. doi: 10.1007/978-3-642-25781-0_78
  11. 11.
    Le T, Tran D, Tran T, Nguyen K, Ma W (2013) Fuzzy entropy semi-supervised support vector data description. In: Proceedings of the international joint conference on neural networks, pp 1–5. doi: 10.1109/IJCNN.2013.6707033
  12. 12.
    Yan Y, Cui J, Pan Z (2013) Semi-supervised fuzzy relational classifier. Comput Intell Des ISCID. doi: 10.1109/ISCID.2013.207 Google Scholar
  13. 13.
    Benbrahim H (2011) Fuzzy Semi-supervised support vector machines. Mach Learn Data Min Pattern Recognit LNCS 6871:127–139CrossRefGoogle Scholar
  14. 14.
    El-Zahhar MM, El-Gayar NF (2010) A semi-supervised learning approach for soft labeled data. In: Proceedings of the 10th international conference on intelligent systems design and applications (ISDA) pp 1136–1141. doi: 10.1109/ISDA.2010.5687034
  15. 15.
    Jamalabadi H, Nasrollahi H, Alizadeh S, Araabi BN, Ahamadabadi MN (2016) Competitive interaction reasoning: a bio-inspired reasoning method for fuzzy rule based classification systems. Inf Sci 352–353:35–47. doi: 10.1016/j.ins.2016.02.052 CrossRefGoogle Scholar
  16. 16.
    Cordeiro FR, Santos WP, Silva-Filho AG (2016) A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Syst Appl 65:116–126CrossRefGoogle Scholar
  17. 17.
    Yan J, Qi W, Yue S, Zhang D, Guo D, Ma H (2016) Application of semi-supervised fuzzy kernel clustering algorithm in recognizing transformer winding’s pressed state. In: ICSPCC 2016—IEEE international conference on signal processing, communications and computing, conference proceedings, 7753697, Hong Kong, China, pp 1–6. doi: 10.1109/ICSPCC.2016.7753697
  18. 18.
    Tanaka D, Honda K, Ubukata S, Notsu A (2016) A semi-supervised framework for MMMs-induced fuzzy co-clustering with virtual samples. Adv Fuzzy Syst 2016:1–8. doi: 10.1155/2016/5206048 MathSciNetCrossRefGoogle Scholar
  19. 19.
    Honda K, Ubukata S, Notsu A, Takahashi N, Ishikawa Y (2015) A semi-supervised fuzzy co-clustering framework and application to twitter data analysis. In: 4th international conference on informatics, electronics and vision, Fukuoka. pp 1–4. doi: 10.1109/ICIEV.2015.7334057
  20. 20.
    Jensen R, Vluymans S, Parthaláin NM, Cornelis C, Saeys Y (2015) Semi-supervised fuzzy-rough feature selection. Lecture notes in computer science including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics 9437:185–195Google Scholar
  21. 21.
    Le T, Nguyen V, Pham T, Dinh M, Le TH (2015) Fuzzy semi-supervised large margin one-class support vector machine. Adv Intell Syst Comput 341:65–78Google Scholar
  22. 22.
    Diaz-Valenzuela I, Vila MA, Martin-Bautista MJ (2016) On the use of fuzzy constraints in semisupervised clustering. IEEE Trans Fuzzy Syst 24(4):992–999CrossRefGoogle Scholar
  23. 23.
    Bchir O, Frigui H, Ismail MMB (2013) Semi-supervised fuzzy clustering with learnable cluster dependent kernels. Int J Artif Intell Tools 22(3):1–26. doi: 10.1142/S0218213013500139 CrossRefGoogle Scholar
  24. 24.
    Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. Adv Artif Int 29(3):93–106Google Scholar
  25. 25.
    Kecman V (2001) Learning and soft computing. MIR Press, Moscow. ISBN 9780262112550zbMATHGoogle Scholar
  26. 26.
    Iliadis L (2007) Intelligent information systems and application in risk estimation. Stamoulis Publishing, ThessalonikiGoogle Scholar
  27. 27.
    Iliadis L, Papaleonidas A (2016) Computational intelligence an intelligent agents. Tziolas publications, ThessalonikiGoogle Scholar
  28. 28.
    Cox E (2005) Fuzzy modeling and genetic algorithms for data mining and exploration. Elsevier Science, USAzbMATHGoogle Scholar
  29. 29.
    Anezakis VD, Dermetzis K, Iliadis L, Spartalis S (2016) Fuzzy cognitive maps for long-term prognosis of the evolution of atmospheric pollution, based on climate change scenarios: The case of Athens. Lecture notes in computer science (lecture notes in artificial intelligence and lecture notes in bioinformatics) 9875:175–186. doi: 10.1007/978-3-319-45243-2_16
  30. 30.
    Ghosh P, Kundu K (2013) Photo-fuzzy concepts generation technique using fuzzy graph. In: Chakraborty MK, Skowron A, Maiti M, Kar S (eds) Facets of uncertainties and applications, ICFUA. Springer, Kolkata, pp 63–72Google Scholar
  31. 31.
    Cordon O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems evolutionary tuning and learning of fuzzy knowledge bases. Advances in fuzzy systems-applications and theory, vol 19. World Scientific Publishing, Hong KongzbMATHGoogle Scholar
  32. 32.
    Pukelsheim F (1994) The three sigma rule. Am Stat 48:88–91MathSciNetGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Institute of Environmental Physics, DOAS GroupUniversity of BremenBremenGermany
  2. 2.Lab of Forest InformaticsDemocritus University of ThraceOrestiadaGreece
  3. 3.School of Engineering, Department of Civil EngineeringDemocritus University of ThraceXanthiGreece

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