Skip to main content

Advertisement

Log in

A new method of mapping relations from data based on artificial neural network

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Many methods have been proposed to derive patterns and correlations from data, such as artificial neural network (ANN) and support vector machine. They successfully learned the pattern from data but failed to illustrate them to human. In this paper, a new method which reveals the influences between factors and identifies key correlations among them from ANN is proposed. The method extracts the relations as relation maps, which is a perceptive illustration to interpret the actual logic beneath the neuron matrices. In this paper, we propose some definitions, express our method in mathematics, and develop a simplified practical algorithm of our method. Then we apply our algorithm to weather forecast problems. The algorithm successfully excavates the relations among weather factors, maps the relations to explicit graph, identifies the key relations, and thus used to reduces the inputs of predicting ANNs by 60 %. Generally, the pruned ANNs performed effectively. Of all the 14 predicting ANNs, when pruned, 10 have prediction errors no more than 16 % greater than the original ANNs, and 5 of the 10 have even lower prediction errors than before. Such result shows that our method successfully identifies key relations among factors. This is a justification for the reliability of our method on extracting relations encoded in ANNs. This promising method can be widely applied in the field of data mining and knowledge discovery.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ahn B, Cho S, Kim C (2000) The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Syst Appl 18(2):65–74

    Article  Google Scholar 

  • Augasta MG, Kathirvalavakumar T (2012a) Reverse engineering the neural networks for rule extraction in classification problems. Neural Process Lett 35(2):131–150

    Article  Google Scholar 

  • Augasta MG, Kathirvalavakumar T (2012b) Rule extraction from neural networks a comparative study. In: International conference on pattern recognition, informatics and medical engineering (PRIME), IEEE, pp 404–408

  • Awan MSK, Awais MM (2011) Predicting weather events using fuzzy rule based system. Appl Soft Comput 11(1):56–63

    Article  Google Scholar 

  • Azam S, Sadiq R (2006) Evaluation of slurry settling rate using fuzzy rule-based modeling. Acta Geotechnica 1(3):149–156

    Article  Google Scholar 

  • Bell JE, Palecki MA, Baker CB et al (2013) US climate reference network soil moisture and temperature observations. J Hydrometeorol 14:977–988

    Google Scholar 

  • Betrie GD, Tesfamariam S, Morin KA, Sadiq R (2012) Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques. Environ Monit Assess 185(5):4171–4182

    Article  Google Scholar 

  • Buckley JJ, Hayashi Y, Czogała E (1993) On the equivalence of neural nets and fuzzy expert systems. Fuzzy Sets Syst 53(2):129–134

    Article  MATH  Google Scholar 

  • Coulibaly P, Evora N (2007) Comparison of neural network methods for infilling missing daily weather records. J Hydrol 341(1C2):27–41

    Article  Google Scholar 

  • Craven MW, Shavlik JW (1991) Visualizing learning and computation in artificial neural networks. Int J Artif Intell Tools 1(3):399–425

    Article  Google Scholar 

  • Craven MW, Shavlik JW (1994) Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the 11th international conference on machine learning, Citeseer, pp 37–45

  • Detienne KB, Detienne DH, Joshi SA (2003) Neural networks as statistical tools for business researchers. Organ Res Methods 6(2):236–265

    Article  Google Scholar 

  • Diamond HJ, Karl TR, Palecki MA et al (2013) US climate reference network after one decade of operations: status and assessment. B Am Meteorol Soc 94:485–498

    Google Scholar 

  • Elsner J, Tsonis A (1992) Nonlinear prediction, chaos, and noise. Bull Am Meteorol Soc 73:49–144

    Article  Google Scholar 

  • Fu LM (1991) Rule learning by searching on adapted nets. In: Proceedings of the ninth national conference on artificial intelligence, pp 590–595

  • Funahashi KI (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2(3):183–192

    Article  Google Scholar 

  • Giorgi MGD, Ficarella A, Tarantino M (2011) Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods. Energy 36(7):3968–3978

    Article  Google Scholar 

  • Hagan MT, Demuth HB, Beale MH et al. (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

  • Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: Proceedings of the international joint conference on neural networks (IJCNN), IEEE, New York, pp 593–605

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366

    Article  Google Scholar 

  • Huang SH, Endsley MR (1997) Providing understanding of the behavior of feedforward neural networks. IEEE Trans Syst Man Cybern B Cybern 27(3):465–474

    Article  Google Scholar 

  • Huang S, Xing H (2002) Extract intelligible and concise fuzzy rules from neural networks. Fuzzy Sets Syst 132(2):233–243

    Article  MathSciNet  MATH  Google Scholar 

  • Hunt K, Sbarbaro D (1991) Neural networks for nonlinear internal model control. IEE Proc D 138(5):431–438

  • Hunter L, Klein T (1993) Finding relevant biomolecular features. In: Proceedings of the first international conference on intelligent systems for molecular biology, AAAI Press, Menlo Park, pp 190–197

  • Im KH, Park SC (2007) Case-based reasoning and neural network based expert system for personalization. Expert Syst Appl 32(1):77–85

    Article  Google Scholar 

  • Islam MS, Zargar A, Dyck R, Mohapatra A, Sadiq R (2012) Data fusion-based risk assessment framework: an example of benzene. Int J Syst Assur Eng Manage 3(4):267–283

    Article  Google Scholar 

  • Ismail MA, Sadiq R, Soleymani HR, Tesfamariam S (2011) Developing a road performance index using a bayesian belief network model. J Frankl Inst 348(9):2539–2555

    Article  Google Scholar 

  • Kahramanli H, Allahverdi N (2009) Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 36(2):1513–1522

    Article  Google Scholar 

  • Kalogirou SA (2000) Applications of artificial neural-networks for energy systems. Appl Energy 67(1C2):17–35

    Article  Google Scholar 

  • Kang B, Deng Y, Sadiq R, Mahadevan S (2012) Evidential cognitive maps. Knowl-Based Syst 35(0):77–86

    Article  Google Scholar 

  • Khashei M, Bijari M (2010) An artificial neural network (p,d,q) model for timeseries forecasting. Expert Syst Appl 37(1):479–489

    Article  Google Scholar 

  • Krasnopolsky VM, Fox-Rabinovitz MS (2006) Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction. Neural Netw 19(2):122–134

    Article  Google Scholar 

  • Kulluk S, Özbakir L, Baykasoğlu A (2013) Fuzzy difaconn-miner: a novel approach for fuzzy rule extraction from neural networks. Expert Syst Appl 40(3):938–946

    Article  Google Scholar 

  • Kuttiyil AS (2004) Survey of rule extraction methods. ETD collection for Wayne State University. Paper AAI1420105

  • Maqsood I, Abraham A (2007) Weather analysis using ensemble of connectionist learning paradigms. Appl Soft Comput 7(3):995–1004

    Article  Google Scholar 

  • Martin JD, Morton YT, Zhou Q (2005) Neural network development for the forecasting of upper atmosphere parameter distributions. Adv Space Res 36(12):2480–2485

    Article  Google Scholar 

  • Mitra S, Hayashi Y (2000) Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans Neural Netw 11(3):748–768

    Article  Google Scholar 

  • Mohamed MH (2011) Rules extraction from constructively trained neural networks based on genetic algorithms. Neurocomputing 74(17):3180–3192

    Article  Google Scholar 

  • Oreski S, Oreski D, Oreski G (2012) Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert Syst Appl 39(16):12,605–12,617

    Article  Google Scholar 

  • Paliwal M, Kumar UA (2009) Neural networks and statistical techniques: a review of applications. Expert Syst Appl 36(1):2–17

    Article  Google Scholar 

  • Sadiq R, Kleiner Y, Rajani B (2007) Water quality failures in distribution networks risk analysis using fuzzy logic and evidential reasoning. Risk Anal 27(5):1381–1394

    Article  Google Scholar 

  • Sadiq R, Tesfamariam S (2008) Developing environmental indices using fuzzy numbers ordered weighted averaging (fn-owa) operators. Stoch Environ Res Risk Assess 22(4):495–505

    Article  MathSciNet  MATH  Google Scholar 

  • Sagdic O, Ozturk I, Kisi O (2012) Modeling antimicrobial effect of different grape pomace and extracts on S. aureus and E. coli in vegetable soup using artificial neural network and fuzzy logic system. Expert Syst Appl 39(8):6792–6798

    Article  Google Scholar 

  • Saito K, Nakano R (1988) Medical diagnostic expert system based on pdp model. In: Proceedings of the IEEE international conference on neural networks, San Diego, 1988, pp 255–262

  • Santos NI, Said AM, James DE, Venkatesh NH (2012) Modeling solar still production using local weather data and artificial neural networks. Renew Energy 40(1):71–79

    Article  Google Scholar 

  • Shin C, Park S (1999) Memory and neural network based expert system. Expert Syst Appl 16(2):145–155

    Article  Google Scholar 

  • Sun Y, Zeng W, Zhao Y, Qi Y, Ma X, Han Y (2010) Development of constitutive relationship model of ti600 alloy using artificial neural network. Comput Mater Sci 48(3):686–691

    Article  Google Scholar 

  • Tang Z, Fishwick PA (1993) Feedforward neural nets as models for time series forecasting. ORSA J Comput 5(4):374–385

    Article  MATH  Google Scholar 

  • Tang B, Flato GM, Holloway G (1994) A study of arctic sea ice and sea-level pressure using pop and neural network methods. Atmos–Ocean 32(3):507–529

    Google Scholar 

  • Tangang F, Hsieh W, Tang B (1997) Forecasting the equatorial pacific sea surface temperatures by neural network models. Clim Dyn 13(2):135–147

    Article  Google Scholar 

  • Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26(12):1249–1256

    Article  Google Scholar 

  • Teschl R, Randeu WL, Teschl F (2007) Improving weather radar estimates of rainfall using feed-forward neural networks. Neural Netw 20(4):519–527

    Article  Google Scholar 

  • Tickle AB, Andrews R, Golea M, Diederich J (1998) The truth will come to light:directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Trans Neural Netw 9(6):1057–1068

    Article  Google Scholar 

  • Uscrn observations. http://www.ncdc.noaa.gov/crn/qcdatasets.html

  • Widrow B, Rumelhart DE, Lehr MA (1994) Neural networks: applications in industry, business and science. Commun ACM 37(3):93–105

    Article  Google Scholar 

  • Xu L (1993) Least mean square error reconstruction principle for self-organizing neural-nets. Neural Netw 6(5):627–648

    Article  Google Scholar 

  • Yigit KS, Ertunc HM (2006) Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks. Int Commun Heat Mass Transf 33(7):898–907

    Article  Google Scholar 

  • Zhang G, Eddy Patuwo B, Y Hu M (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14(1):35–62

    Article  Google Scholar 

  • Zurada JM (1992) Introduction to artificial neural systems, vol 408. West, St Paul

    Google Scholar 

Download references

Acknowledgments

The work is partially supported by National Natural Science Foundation of China (Grant No. 61174022), Chongqing Natural Science Foundation (Grant No. CSCT, 2010BA2003), Program for New Century Excellent Talents in University (Grant No. NCET-08-0345), Doctor Funding of Southwest University (Grant No. SWU110021).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Deng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, Y., Liu, J., Bao, Q. et al. A new method of mapping relations from data based on artificial neural network. Int J Syst Assur Eng Manag 5, 544–553 (2014). https://doi.org/10.1007/s13198-013-0204-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-013-0204-3

Keywords

Navigation