Comparison of classification techniques used in machine learning as applied on vocational guidance data

Bülbül H. İ., Ünsal Ö.

10th International Conference on Machine Learning and Applications, ICMLA 2011, Honolulu, HI, United States Of America, 18 - 21 December 2011, vol.2, pp.298-301 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2
  • Doi Number: 10.1109/icmla.2011.49
  • City: Honolulu, HI
  • Country: United States Of America
  • Page Numbers: pp.298-301
  • Keywords: classification techniques, data mining, energy applications, machine learning
  • Gazi University Affiliated: Yes

Abstract

Recent developments in information systems as well as computerization of business processes by organizations have led to a faster, easier and more accurate data analysis. Data mining and machine learning techniques have been used increasingly in the analysis of data in various fields ranging from medicine to finance, education and energy applications. Machine learning techniques make it possible to deduct meaningful further information from those data processed by data mining. Such meaningful and significant information helps organizations to establish their future policies on a sounder basis, and to gain major advantages in terms of time and cost. This study applies classification algorithms used in data mining and machine learning techniques on those data obtained from individuals during the vocational guidance process, and tries to determine the most appropriate algorithm. © 2011 IEEE.