Advertisement

Overview on Multivariate Adaptive Regression Splines

  • Kweku-Muata Osei-Bryson
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 34)

Abstract

This chapter provides an overview of multivariate adaptive regression splines (MARS). Its main purpose is to introduce the reader to the major concepts underlying this data mining technique, particularly those that are relevant to the chapter that involves the use of this technique. This chapter includes an illustrative example and also provides guidance for interpreting a MARS model.

References

  1. Balshi MS, McGuire AD, Duffy P, Flannigan M, Walsh J, Melillo J (2009) Assessing the response of area burned to changing climate in Western Boreal North America using a multivariate adaptive regression splines (MARS) approach. Glob Change Biol 15(3):578–600CrossRefGoogle Scholar
  2. Behera AK, Verbert J, Lauwers B, Duflou JR (2012) Tool path compensation strategies for single point incremental sheet forming using multivariate adaptive regression splines. Comput-Aided Des 45(3):575–590CrossRefGoogle Scholar
  3. Breiman L, Friedman J, Olshen R, Charles S (1984) Classification and regression trees. Wadsworth International GroupGoogle Scholar
  4. Briand L, Freimut B, Vollei F (2004) Using multiple adaptive regression splines to understand trends in inspection data and identify optimal inspection rates. J Syst Softw 73(2):2–23CrossRefGoogle Scholar
  5. De Andrés J, Lorca P, de Cos Juez FJ, Sánchez-Lasheras F (2011) Bankruptcy forecasting: a hybrid approach using fuzzy c-means clustering and multivariate adaptive regression splines (MARS). Expert Syst Appl 38(3):1866–1875CrossRefGoogle Scholar
  6. Deconinck E, Coomans D, Vander Heyden Y (2007) Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs. J Pharm Biomed Anal 43(1):119–130CrossRefGoogle Scholar
  7. Friedman JH (1991) Multivariate Adaptive Regression Splines. Ann Stat 19(1):1, pp 1–141Google Scholar
  8. Guo W, Zhao N, Shao H (2010) IT investment efficiency analysis of equipment manufacturing industry based on two-stage nonparametric model. In: Proceedings of IEEE 2010 international conference on challenges in environmental science and computer engineering, vol 2, pp 21–24Google Scholar
  9. Hastie T, Tibshirani R (1990) Generalized additive model. Chapman and Hall, LondonGoogle Scholar
  10. Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer-Verlag, New YorkCrossRefGoogle Scholar
  11. Hu Y, Loizou PC (2008) Evaluation of objective quality measures for speech enhancement. IEEE Trans Audio Speech Lang Process 16(1):229–238CrossRefGoogle Scholar
  12. Hung Y-H, Chou S-C, Tzeng G-H (2011) Knowledge management adoption and assessment for SMES by a novel MCDM approach. Decis Support Syst 51:270–291CrossRefGoogle Scholar
  13. Ko M, Osei-Bryson K (2004) Using regression splines to assess the impact of information technology investments on productivity in the healthcare industry. Inf Syst J 14:43–63CrossRefGoogle Scholar
  14. Ko M, Clark JG, Ko D (2008) Revisiting the impact of information technology investments on productivity: an empirical investigation using multivariate adaptive regression splines. Inf Res Manage J 21(3):1–23CrossRefGoogle Scholar
  15. Kositanurit B, Ngwenyama O, Osei-Bryson K-M (2006) An exploration of factors that impact individual performance in an ERP environment: an analysis using multiple analytical techniques. EurJ Inf Syst 15:556–568CrossRefGoogle Scholar
  16. Leathwick JR, Rowe D, Richardson J, Elith J, Hastie T (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshw Biol 50(12):2034–2052CrossRefGoogle Scholar
  17. Morawczynski O, Ngwenyama O (2007) Unraveling the impact of investments in ICT, education and health on development: an analysis of archival data of five West African countries using regression splines. Electron J Inf Syst Dev Countries 29:1–15Google Scholar
  18. Mukkamala S, Sung AH, Abraham A, Ramos V (2006) Intrusion detection systems using adaptive regression spines. In: Enterprise Information Systems VI, pp 211–218. Springer, NetherlandsGoogle Scholar
  19. Osei-Bryson K-M, Dong L, Ngwenyama O (2008) Exploring managerial factors affecting ERP implementation: an investigation of the Klein-Sorra model using regression splines. Inf Syst J 18(5):499–527CrossRefGoogle Scholar
  20. Martin A (2011) A Hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and MARS. Int J Soft Comput 2(1):12–24CrossRefGoogle Scholar
  21. Zhou Y, Leung H (2007) Predicting object-oriented software maintainability using multivariate adaptive regression splines. J Syst Softw 80(8):1349–1361CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Information SystemsVirginia Commonwealth UniversityRichmondUSA

Personalised recommendations