Advertisement

Modeling reverse thinking for machine learning

  • Huihui Li
  • Guihua WenEmail author
Methodologies and Application
  • 4 Downloads

Abstract

Human inertial thinking schemes can be formed through learning, which are then applied to quickly solve similar problems later. However, when problems are significantly different, inertial thinking generally presents the solutions that are definitely imperfect. In such cases, people will apply creative thinking, such as reverse thinking, to solve problems. Similarly, machine learning methods also form inertial thinking schemes through learning the knowledge from a large amount of data. However, when the testing samples are vastly different, the formed inertial thinking schemes will inevitably generate errors. This kind of inertial thinking is called illusion inertial thinking. Because all machine learning methods do not consider the illusion inertial thinking, in this paper we propose a new method that uses the reverse thinking to correct the illusion inertial thinking, which increases the generalization ability of machine learning methods. Experimental results on benchmark data sets validated the proposed method.

Keywords

Machine learning Inertial thinking model Modeling reverse thinking 

Notes

Funding

This study was supported by the China National Science Foundation (60973083/61273363), Science and Technology Planning Project of Guangdong Province (2014A010103009/2015A020217002), and Guangzhou Science and Technology Planning Project (201504291154480, 201604020179, 201803010088).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This paper does not contain any studies with human or animals participants.

References

  1. Agnoli S (2018) The emotionally intelligent use of attention and affective arousal under creative frustration and creative success. Personal Ind Differ.  https://doi.org/10.1016/j.paid.2018.04.041 Google Scholar
  2. Alcal-Fdez J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Log Soft Comput 17:255–287Google Scholar
  3. Ayoubi S, Limam N, Salahuddin MA, Shahriar N et al (2018) Machine learning for cognitive network management. IEEE Commun Mag 56:158–164CrossRefGoogle Scholar
  4. Carlson T, Goddard E, Kaplan DM, Klein C, Ritchie JB (2018) Ghosts in machine learning for cognitive neuroscience: moving from data to theory. NeuroImage 180:88–100CrossRefGoogle Scholar
  5. Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TISJ) 2(27):1–27Google Scholar
  6. Chiu F-C, Hsu C-C, Lin Y-N, Chen H-C, Liu C-H (2017) Effects of the interaction between exercising self-control and PhoPhiKat on divergent and convergent thinking. Think Skills Creat 24:286–298CrossRefGoogle Scholar
  7. Coccoli M, Maresca P, Stanganelli L (2017) The role of big data and cognitive computing in the learning process. J Vis Lang Comput 38:97–103CrossRefGoogle Scholar
  8. Colzato LS, Ritter SM, Steenbergen L (2018) Transcutaneous vagus nerve stimulation (tVNS) enhances divergent thinking. Neuropsychologia 111:72–76CrossRefGoogle Scholar
  9. Corcoran K, Hundhammer T, Mussweiler T (2009) A tool for thought! when comparative thinking reduces stereotyping effects. J Exp Soc Psychol 45(4):1008–1011CrossRefGoogle Scholar
  10. DeMotta Y, Chao MC, Kramer T (2016) The effect of dialectical thinking on the integration of contradictory information. J Consum Psychol 26(1):40–52CrossRefGoogle Scholar
  11. Guegan D, Hassani B (2018) Regulatory learning: how to supervise machine learning models? an application to credit scoring. J Finance Data Sci 4:157–171CrossRefGoogle Scholar
  12. Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58(301):13–30MathSciNetCrossRefzbMATHGoogle Scholar
  13. Jankowska DM (2018) Family factors and development of creative thinking. Personal Ind Differ.  https://doi.org/10.1016/j.paid.2018.07.030 Google Scholar
  14. Ji Y, Chen Y, Fu H, Yang G (2017) An EnKF-based scheme to optimize hyper-parameters and features for SVM classifier. Pattern Recognit 62:202–213CrossRefGoogle Scholar
  15. Koopmanschap R, Hoogendoorn M, Roessingh JJ (2015) Tailoring a cognitive model for situation awareness using machine learning. Appl Intell 42:36–48CrossRefGoogle Scholar
  16. Kutlu Y, Yayık A, Yildirim E, Yildirim S (2017) LU triangularization extreme learning machine in EEG cognitive task classification. Neural Comput Appl.  https://doi.org/10.1007/s00521-017-3142-1 Google Scholar
  17. Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–1338MathSciNetCrossRefzbMATHGoogle Scholar
  18. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  19. Madl T, Franklin S, Chen K, Trappl R (2018) A computational cognitive framework of spatial memory in brains and robots. Cogn Syst Res 47:147–172CrossRefGoogle Scholar
  20. Martin M, Lebiere C, Fields MA, Lennon C (2018) Learning features while learning to classify: a cognitive model for autonomous systems. Comput Math Org Theory.  https://doi.org/10.1007/s10588-018-9279-3 Google Scholar
  21. Mirza B, Lin Z (2016) Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Netw 80:79–94CrossRefGoogle Scholar
  22. Mohammadi M, Al-Fuqaha A (2018) Enabling cognitive smart cities using big data and machine learning: approaches and challenges. IEEE Commun Mag 56:94–101CrossRefGoogle Scholar
  23. Montag-Smit T, Maertz CP Jr (2017) Searching outside the box in creative problem solving: the role of creative thinking skills and domain knowledge. J Bus Res 81:1–10CrossRefGoogle Scholar
  24. Myerson J, Down S (2016) Why designers need to reverse their thinking. J Des Econ Innov 2(4):288–299Google Scholar
  25. Napoles G, Falcon R, Papageorgiou E, Bello R, Vanhoof K (2017) Rough cognitive ensembles. Int J Approx Reason 85:79–96MathSciNetCrossRefzbMATHGoogle Scholar
  26. Paul GK, Smart R (2018) Human-extended machine cognition. Cogn Syst Res 49:9–23CrossRefGoogle Scholar
  27. Pratama M, Zhang G, Er MJ, Anavatti S (2017) An incremental type-2 meta-cognitive extreme learning machine. IEEE Trans Cybern 47(2):339–353Google Scholar
  28. Ruiz FJ, Agell N, Angulo C, Sánchez M (2018) A learning system for adjustment processes based on human sensory perceptions. Cogn Syst Res 52:58–66CrossRefGoogle Scholar
  29. Savitha R, Suresh S, Kim HJ (2014) A meta-cognitive learning algorithm for an extreme learning machine classifier. Cogn Comput 6(2):253–263CrossRefGoogle Scholar
  30. Sawaguchi M (2015) Research on the efficacy of creative risk management approach based on reverse thinking. Proc Eng 131:577–589CrossRefGoogle Scholar
  31. Smith MR, Martinez T, Giraud-Carrier C (2014) An instance level analysis of data complexity. Mach Learn 95(2):225–256MathSciNetCrossRefGoogle Scholar
  32. Souillard-Mandar W, Davis R, Rudin C et al (2016) Learning classification models of cognitive conditions from subtle behaviors in the digital clock drawing test. Mach Learn 102:393–441MathSciNetCrossRefGoogle Scholar
  33. Spruyt B, Van Droogenbroeck F, van Noord J (2018) Conflict thinking: exploring the social basis of perceiving the world through the lens of social conflict. Soc Sci Res 74:16–29CrossRefGoogle Scholar
  34. Steele LM, Johnson G, Medeiros KE (2018) Looking beyond the generation of creative ideas: confidence in evaluating ideas predicts creative outcomes. Personal Ind Differ 125:21–29CrossRefGoogle Scholar
  35. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks. ICLR. arXiv:1312.6199
  36. Thoring K, Desmet P, Badke-Schaub P (2018) Creative environments for design education and practice: a typology of creative spaces. Des Stud 56:54–83CrossRefGoogle Scholar
  37. Toivonen H, Gross O (2015) Data mining and machine learning in computational creativity. WIREs Data Min Knowl Discov 5:265–275CrossRefGoogle Scholar
  38. Vahdat M, Oneto L, Anguita D, Funk M, Rauterberg M (2016) Can machine learning explain human learning? Neurocomputing 192:14–28CrossRefGoogle Scholar
  39. van Leeuwen J, Wiedermann J (2018) Question answering by humans and machines: a complexity-theoretic view. Theor Comput Sci.  https://doi.org/10.1016/j.tcs.2018.08.012 Google Scholar
  40. Wechsler SM, Saiz C, Rivas SF et al (2018) Creative and critical thinking: independent or overlapping components? Think Skills Creat 27:114–122CrossRefGoogle Scholar
  41. Wen G, Wei J, Wang J, Zhou T, Chen L (2013) Cognitive gravitation model for classification on small noisy data. Neurcomputing 118:245–252CrossRefGoogle Scholar
  42. Xie Z, Jin Y (2018) An extended reinforcement learning framework to model cognitive development with enactive pattern representation. IEEE Trans Cogn Dev Syst 10:738–750CrossRefGoogle Scholar
  43. Yu-Shan C, Hung-Chang L, Yu-Hung C, Wan-Hsuan Y (2018) Effects of creative components and creative behavior on design creativity. Think Skills Creat 29:23–31CrossRefGoogle Scholar
  44. Zhang Y, Er MJ (2016) Sequential active learning using meta-cognitive extreme learning machine. Neurocomputing 173:835–844CrossRefGoogle Scholar
  45. Zhang H, Berg AC, Maire M, Malik J (2006) SVM-KNN: discriminative nearest neighbor classification for visual category recognition. In: 2006 IEEE Computer Society conference on computer vision and pattern recognition, New York, 17–22 June 2006, pp 2126–2136.  https://doi.org/10.1109/CVPR.2006.301

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations