Skip to main content
SpringerLink
Log in

EML for Unsupervised Learning

  • Chapter
  • First Online:
Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

  • 1549 Accesses

Abstract

This chapter introduces the use of Evolutionary Machine Learning (EML) techniques for unsupervised machine learning tasks. First, a brief introduction to the main concepts related to unsupervised Machine Learning (ML) is presented. Then, an overview of the main EML approaches to these tasks is given together with a discussion of the main achievements and current challenges in addressing these tasks. We focus on four commonly found unsupervised learning tasks: Data preparation, Outlier detection, Dimensionality reduction, and Association rule mining. Finally, we present a number of findings from the review. These findings could guide the reader at the time of applying EML techniques to unsupervised ML tasks or when developing new EML approaches.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyingOptions = buybox.querySelectorAll(".buying-option") ;[].slice.call(buyingOptions).forEach(initCollapsibles) var buyboxMaxSingleColumnWidth = 480 function initCollapsibles(subscription, index) { var toggle = subscription.querySelector(".buying-option-price") subscription.classList.remove("expanded") var form = subscription.querySelector(".buying-option-form") var priceInfo = subscription.querySelector(".price-info") var buyingOption = toggle.parentElement if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth <= buyboxMaxSingleColumnWidth && option != buyingOption) { hideBuyingOption(option) } }) var expanded = toggle.getAttribute("aria-expanded") === "true" || false toggle.setAttribute("aria-expanded", !expanded) form.hidden = expanded if (!expanded) { buyingOption.classList.add("expanded") } else { buyingOption.classList.remove("expanded") } priceInfo.hidden = expanded }, false) } } function hideBuyingOption(buyingOption) { var toggle = buyingOption.querySelector(".buying-option-price") var form = buyingOption.querySelector(".buying-option-form") var priceInfo = buyingOption.querySelector(".price-info") toggle.setAttribute("aria-expanded", false) form.hidden = true buyingOption.classList.remove("expanded") priceInfo.hidden = true } function initKeyControls() { document.addEventListener("keydown", function (event) { if (document.activeElement.classList.contains("buying-option-price") && (event.code === "Space" || event.code === "Enter")) { if (document.activeElement) { event.preventDefault() document.activeElement.click() } } }, false) } function initialStateOpen() { var buyboxWidth = buybox.offsetWidth ;[].slice.call(buybox.querySelectorAll(".buying-option")).forEach(function (option, index) { var toggle = option.querySelector(".buying-option-price") var form = option.querySelector(".buying-option-form") var priceInfo = option.querySelector(".price-info") if (buyboxWidth > buyboxMaxSingleColumnWidth) { toggle.click() } else { if (index === 0) { toggle.click() } else { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdella, M., Marwala, T.: The use of genetic algorithms and neural networks to approximate missing data in database. In: IEEE 3rd International Conference on Computational Cybernetics, 2005, pp. 207–212. IEEE (2005)

    Google Scholar 

  2. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)

    Google Scholar 

  3. Al-Helali, B., Chen, Q., Xue, B., Zhang, M.: Gp with a hybrid tree-vector representation for instance selection and symbolic regression on incomplete data. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 604–611. IEEE (2021)

    Google Scholar 

  4. Al-Helali, B., Chen, Q., Xue, B., Zhang, M.: A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data. Soft. Comput. 25(8), 5993–6012 (2021)

    Article  Google Scholar 

  5. Albuquerque, I.M.R., Nguyen, B.H., Xue, B., Zhang, M.: A novel genetic algorithm approach to simultaneous feature selection and instance selection. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 616–623. IEEE (2020)

    Google Scholar 

  6. Andreassen, A., Feige, I., Frye, C., Schwartz, M.D.: JUNIPR: a framework for unsupervised machine learning in particle physics. Eur. Phys. J. C 79, 1–24 (2019)

    Article  Google Scholar 

  7. Badhon, B., Jahangir, M.M., Kabir, S.X., Kabir, M.: A survey on association rule mining based on evolutionary algorithms. Int. J. Comput. Appl. 43(8), 775–785 (2021)

    Google Scholar 

  8. Bandyopadhyay, S., Santra, S.: A genetic approach for efficient outlier detection in projected space. Pattern Recogn. 41(4), 1338–1349 (2008)

    Article  MATH  Google Scholar 

  9. Beiranvand, V., Mobasher-Kashani, M., Bakar, A.A.: Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. Expert Syst. Appl. 41(9), 4259–4273 (2014)

    Article  Google Scholar 

  10. Berg-Kirkpatrick, T., Bouchard-Côté, A., DeNero, J., Klein, D.: Painless unsupervised learning with features. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 582–590 (2010)

    Google Scholar 

  11. Cano, J.R., Herrera, F., Lozano, M.: Instance selection using evolutionary algorithms: an experimental study. In: Advanced Techniques in Knowledge Discovery and Data Mining, pp. 127–152 (2005)

    Google Scholar 

  12. Casolla, G., Cuomo, S., Cola, V.S.D., Piccialli, F.: Exploring unsupervised learning techniques for the internet of things. IEEE Trans. Industr. Inf. 16(4), 2621–2628 (2019)

    Article  Google Scholar 

  13. Chen, Q., Huang, M., Wang, H., Guangquan, X.: A feature discretization method based on fuzzy rough sets for high-resolution remote sensing big data under linear spectral model. IEEE Trans. Fuzzy Syst. 30(5), 1328–1342 (2021)

    Article  Google Scholar 

  14. Crawford, K.D., Wainwright, R.L.: Applying genetic algorithms to outlier detection. In: Proceedings of The Sixth International Conference on Genetic Algorithms (ICGA-1995), pp. 546–550 (1995)

    Google Scholar 

  15. Cucina, D., Di Salvatore, A., Protopapas, M.K.: Outliers detection in multivariate time series using genetic algorithms. Chemometr. Intell. Labor. Syst. 132, 103–110 (2014)

    Article  Google Scholar 

  16. Dai, Y., Xue, B., Zhang, M.: New representations in PSO for feature construction in classification. In: Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23–25, 2014, Revised Selected Papers 17, pp. 476–488. Springer (2014)

    Google Scholar 

  17. de Melo, V.V., Banzhaf, W.: Kaizen programming for feature construction for classification. In: Genetic Programming Theory and Practice XIII, pp. 39–57 (2016)

    Google Scholar 

  18. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  19. Derrac, J., García, S., Herrera, F.: A survey on evolutionary instance selection and generation. In: Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, pp. 233–266. IGI Global (2012)

    Google Scholar 

  20. Drozdz, K., Kwasnicka, H.: Feature set reduction by evolutionary selection and construction. In: In Proceedings of the 4th KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA-2010, Part II, pp. 140–149. Springer (2010)

    Google Scholar 

  21. Eklund, N.H.W.: Using genetic algorithms to estimate confidence intervals for missing spatial data. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) 36(4), 519–523 (2006)

    Google Scholar 

  22. García, J.C.F., Kalenatic, D., Bello, C.A.L.: Missing data imputation in time series by evolutionary algorithms. In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence: Proceedings of the 4th International Conference on Intelligent Computing, ICIC-2008, pp. 275–283. Springer (2008)

    Google Scholar 

  23. Flores, J.L., Inza, I., Larrañaga, P.: Wrapper discretization by means of estimation of distribution algorithms. Intell. Data Anal. 11(5), 525–545 (2007)

    Article  Google Scholar 

  24. García, S., López, V., Luengo, J., Carmona, C.J., Herrera, F.: A preliminary study on selecting the optimal cut points in discretization by evolutionary algorithms. In: International Conference on Pattern Recognition Applications and Methods (ICPRAM-2012), pp. 211–216 (2012)

    Google Scholar 

  25. Garciarena, U., Mendiburu, A., Santana, R.: Towards a more efficient representation of imputation operators in TPOT (2018). CoRR, arXiv:1801.04407

  26. Garciarena, U., Santana, R.: An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers. Expert Syst. Appl. 89, 52–65 (2017)

    Article  Google Scholar 

  27. Garciarena, U., Santana, R., Mendiburu, A.: Evolving imputation strategies for missing data in classification problems with TPOT (2017). CoRR, arXiv:1706.01120

  28. Garciarena, U., Santana, R., Mendiburu, A.: Analysis of the complexity of the automatic pipeline generation problem. In: IEEE Congress on Evolutionary Computation (CEC-2018), pp. 1–8. IEEE (2018)

    Google Scholar 

  29. Ghanem, T.F., Elkilani, W.S., Abdul-Kader, H.M.: A hybrid approach for efficient anomaly detection using metaheuristic methods. J. Adv. Res. 6(4), 609–619 (2015)

    Article  Google Scholar 

  30. Ghosh, A., Nath, B.: Multi-objective rule mining using genetic algorithms. Inf. Sci. 163(1–3), 123–133 (2004)

    Article  MathSciNet  Google Scholar 

  31. Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining-a general survey and comparison. ACM SIGKDD Explorat. Newsl 2(1), 58–64 (2000)

    Article  Google Scholar 

  32. Horváth, L., Hušková, M.: Change-point detection in panel data. J. Time Ser. Anal. 33(4), 631–648 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhengping, H., Li, Z., Wang, X., Zheng, S.: Unsupervised descriptor selection based meta-learning networks for few-shot classification. Pattern Recogn. 122, 108304 (2022)

    Article  Google Scholar 

  34. Huang, M.W., Lin, W.C., Tsai, C.F.: Outlier removal in model-based missing value imputation for medical datasets. J. Healthcare Eng. 2018 (2018)

    Google Scholar 

  35. Inza, I., Larrañaga, P., Etxeberria, R., Sierra, B.: Feature subset selection by Bayesian network-based optimization. Artif. Intell. 123(1–2), 157–184 (2000)

    Article  MATH  Google Scholar 

  36. Inza, I., Merino, M., Larranaga, P., Quiroga, J., Sierra, B., Girala, M.: Feature subset selection by genetic algorithms and estimation of distribution algorithms: a case study in the survival of cirrhotic patients treated with TIPS. Artif. Intell. Med. 23(2), 187–205 (2001)

    Article  MATH  Google Scholar 

  37. Kashef, S., Nezamabadi-pour, H.: An advanced ACO algorithm for feature subset selection. Neurocomputing 147, 271–279 (2015)

    Article  Google Scholar 

  38. Kim, K., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst. Appl. 19(2), 125–132 (2000)

    Article  Google Scholar 

  39. Kim, Y.S., Nick Street, W., Menczer, F.: Evolutionary model selection in unsupervised learning. Intell. Data Anal. 6(6), 531–556 (2002)

    Article  MATH  Google Scholar 

  40. Kordos, M., Blachnik, M., Scherer, R.: Fuzzy clustering decomposition of genetic algorithm-based instance selection for regression problems. Inf. Sci. 587, 23–40 (2022)

    Article  Google Scholar 

  41. Kordos, M., Łapa, K.: Multi-objective evolutionary instance selection for regression tasks. Entropy 20(10), 746 (2018)

    Article  Google Scholar 

  42. Kotsiantis, S., Kanellopoulos, D.: Discretization techniques: a recent survey. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 47–58 (2006)

    Google Scholar 

  43. Krawiec, K.: Genetic programming-based construction of features for machine learning and knowledge discovery tasks. Genet. Program Evolvable Mach. 3, 329–343 (2002)

    Article  MATH  Google Scholar 

  44. Krishna, M., Ravi, V.: Particle swarm optimization and covariance matrix based data imputation. In: 2013 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–6. IEEE (2013)

    Google Scholar 

  45. Kuo, R.J., Chao, C.M., Chiu, Y.T.: Application of particle swarm optimization to association rule mining. Appl. Soft Comput. 11(1), 326–336 (2011)

    Article  Google Scholar 

  46. Kwedlo, W., Kretowski, M.: An evolutionary algorithm using multivariate discretization for decision rule induction. In: In Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery, PKDD-99, pp. 392–397. Springer (1999)

    Google Scholar 

  47. Larrañaga, P., Karshenas, H., Bielza, C., Santana, R.: A review on probabilistic graphical models in evolutionary computation. J. Heurist. 18(5), 795–819 (2012)

    Article  MATH  Google Scholar 

  48. Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston (2002)

    MATH  Google Scholar 

  49. Leardi, R.: Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. J. Chemom. 8(1), 65–79 (1994)

    Article  Google Scholar 

  50. Lensen, A., Xue, B., Zhang, M.: Can genetic programming do manifold learning too? In Proceedings of the 22nd European Conference on Genetic Programming, EuroGP-2019, pp. 114–130. Springer (2019)

    Google Scholar 

  51. Lensen, A., Xue, B., Zhang, M.: Genetic programming for manifold learning: preserving local topology. IEEE Trans. Evol. Comput. 26(4), 661–675 (2021)

    Article  Google Scholar 

  52. Lensen, A., Zhang, M., Xue, B.: Multi-objective genetic programming for manifold learning: balancing quality and dimensionality. Genet. Program Evolvable Mach. 21(3), 399–431 (2020)

    Article  Google Scholar 

  53. Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D.: Machine learning in agriculture: A review. Sensors 18(8), 2674 (2018)

    Article  Google Scholar 

  54. Lillywhite, K., Lee, D.-J., Tippetts, B., Archibald, J.: A feature construction method for general object recognition. Pattern Recogn. 46(12), 3300–3314 (2013)

    Article  Google Scholar 

  55. Lobato, F., Sales, C., Araujo, I., Tadaiesky, V., Dias, L., Ramos, L., Santana, A.: Multi-objective genetic algorithm for missing data imputation. Pattern Recogn. Lett. 68, 126–131 (2015)

    Article  Google Scholar 

  56. Ma, J., Gao, X.: A filter-based feature construction and feature selection approach for classification using genetic programming. Knowl.-Based Syst. 196, 105806 (2020)

    Google Scholar 

  57. Metodiev, E.M., Nachman, B., Thaler, J.: Classification without labels: Learning from mixed samples in high energy physics. J. High Energy Phys. 2017(10), 1–18 (2017)

    Article  Google Scholar 

  58. Mohemmed, A.W., Zhang, M., Browne, W.N.: Particle swarm optimisation for outlier detection. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 83–84 (2010)

    Google Scholar 

  59. Muharram, M.A., Smith, G.D.: Evolutionary feature construction using information gain and gini index. In: Proceedings of the 7th European Conference on Genetic Programming, EuroGP-2004, pp. 379–388. Springer (2004)

    Google Scholar 

  60. Neshatian, K., Zhang, M.: Dimensionality reduction in face detection: a genetic programming approach. In: 24th International Conference on Image and Vision Computing, pp. 391–396. IEEE (2009)

    Google Scholar 

  61. Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VI, pp. 69–84. Springer (2016)

    Google Scholar 

  62. Olson, R.S., Moore, J.H.: TPOT: A tree-based pipeline optimization tool for automating machine learning. In: Workshop on Automatic Machine Learning, pp. 66–74. PMLR (2016)

    Google Scholar 

  63. Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Kittler, J.: A review of instance selection methods. Artif. Intell. Rev. 34, 133–143 (2010)

    Article  Google Scholar 

  64. Orzechowski, P., Magiera, F., Moore, J.H.: Benchmarking manifold learning methods on a large collection of datasets. In: Proceedings of the 23rd European Conference on Genetic Programming, pp. 135–150. Springer (2020)

    Google Scholar 

  65. Otero, F.E.B., Silva, M.M.S., Freitas, A.A. and Nievola, J.C.: Genetic programming for attribute construction in data mining. In: In Proceedings of the 6th European Conference on Genetic Programming, EuroGP-2003, pp. 384–393. Springer (2003)

    Google Scholar 

  66. Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  67. Ramírez-Gallego, S., García, S., Benítez, J.M., Herrera, F.: Multivariate discretization based on evolutionary cut points selection for classification. IEEE Trans. Cybern. 46(3), 595–608 (2015)

    Article  Google Scholar 

  68. Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evolut. Comput. 4(2), 164–171 (2000)

    Article  Google Scholar 

  69. Saeys, Y., Degroeve, S., Aeyels, D., Van de Peer, Y., Rouzé, P.: Fast feature selection using a simple estimation of distribution algorithm: A case study on splice site prediction. Bioinformatics 19(2), ii179–ii188 (2003)

    Google Scholar 

  70. Said, R., Elarbi, M., Bechikh, S., Coello, C.A.C., Said, L.B.: Discretization-based feature selection as a bi-level optimization problem. IEEE Trans. Evolut, Comput (2022)

    Google Scholar 

  71. Shelton, J., Dozier, G., Bryant, K., Small, L., Adams, J., Popplewell, K., Abegaz, T., Alford, A., Woodard, D.L. and Ricanek, K.: Genetic and evolutionary feature extraction via X-TOOLS. In: Proceedings of the International Conference on Genetic and Evolutionary Methods (GEM), p. 1 (2011)

    Google Scholar 

  72. Telikani, A., Gandomi, A.H., Shahbahrami, A.: A survey of evolutionary computation for association rule mining. Inf. Sci. 524, 318–352 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  73. Tolvi, J.: Genetic algorithms for outlier detection and variable selection in linear regression models. Soft. Comput. 8, 527–533 (2004)

    Article  MATH  Google Scholar 

  74. Tran, C.T., Zhang, M., Andreae, P., Xue, B.: Multiple imputation and genetic programming for classification with incomplete data. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 521–528 (2017)

    Google Scholar 

  75. Uriot, T., Virgolin, M., Alderliesten, T. and Bosman, P.A.N.: On genetic programming representations and fitness functions for interpretable dimensionality reduction. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 458–466 (2022)

    Google Scholar 

  76. Vafaie, H., De Jong, K.: Genetic algorithms as a tool for restructuring feature space representations. In: Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, pp. 8–11. IEEE (1995)

    Google Scholar 

  77. Van Der Maaten, L., Postma, E., Van den Herik, J., et al.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10(66–71), 13 (2009)

    Google Scholar 

  78. Ventura, S., Luna, J.M.: Pattern Mining with Genetic Algorithms, pp. 63–85. Springer International Publishing, Cham (2016)

    Google Scholar 

  79. Wakabi-Waiswa, P.P., Baryamureeba, V.: Extraction of interesting association rules using genetic algorithms. Int. J. Comput. ICT Res. 2(1), 26–33 (2008)

    Google Scholar 

  80. Wang, J., Biljecki, F.: Unsupervised machine learning in urban studies: a systematic review of applications. Cities 129, 103925 (2022)

    Article  Google Scholar 

  81. Wu, S.X., Banzhaf, W.: The use of computational intelligence in intrusion detection systems: A review. Appl. Soft Comput. 10(1), 1–35 (2010)

    Article  Google Scholar 

  82. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evolut. Comput. 20(4), 606–626 (2015)

    Article  Google Scholar 

  83. Xue, B., Zhang, M., Dai, Y., Browne, W.N.: PSO for feature construction and binary classification. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 137–144 (2013)

    Google Scholar 

  84. Yan, X., Zhang, C., Zhang, S.: Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst. Appl. 36(2), 3066–3076 (2009)

    Article  Google Scholar 

  85. Yin, J., Wang, Y., Jiankun, H.: A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy. IEEE Trans. Industr. Inf. 8(4), 935–943 (2012)

    Article  Google Scholar 

  86. Zhang, M., Lee, D.-J.: Efficient training of evolution-constructed features. In: Proceedings of the 11th International Symposium on Advances in Visual Computing, ISVC-2015, Part II, pp. 646–654. Springer (2015)

    Google Scholar 

  87. Zhang, M., Gong, M., Chan, Y.: Hyperspectral band selection based on multi-objective optimization with high information and low redundancy. Appl. Soft Comput. 70, 604–621 (2018)

    Article  Google Scholar 

  88. Zhao, Q., Bhowmick, S.S.: Association Rule Mining: A Survey, vol. 135. Nanyang Technological University, Singapore (2003)

    Google Scholar 

  89. Zhou, M., Duan, N., Liu, S., Shum, H.-Y.: Progress in neural NLP: modeling, learning, and reasoning. Engineering 6(3), 275–290 (2020)

    Article  Google Scholar 

  90. Zhou, Y., Kang, J., Kwong, S., Wang, X., Zhang, Q.: An evolutionary multi-objective optimization framework of discretization-based feature selection for classification. Swarm Evol. Comput. 60, 100770 (2021)

    Article  Google Scholar 

  91. Zhu, W., Wang, J., Zhang, Y., Jia, L.: A discretization algorithm based on information distance criterion and ant colony optimization algorithm for knowledge extracting on industrial database. In: 2010 IEEE International Conference on Mechatronics and Automation, pp. 1477–1482. IEEE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of the Basque Country (UPV/EHU), San Sebastian, Gipuzkoa, Spain

    Roberto Santana

Authors
  1. Roberto Santana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roberto Santana .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Santana, R. (2024). EML for Unsupervised Learning. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3814-8_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3813-1

  • Online ISBN: 978-981-99-3814-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation

-