Title :
Determination of Vocational Fields with Machine Learning Algorithm
Author :
Halil Ibrahim Bulbul;Ozkan Unsal
Author_Institution :
Dept. of Comput. Educ., Gazi Univ., Ankara, Turkey
Abstract :
The importance of vocational and technical training is growing day by day in parallel to the developing technology. It is inevitable to utilise opportunities presented by information and communication technologies in order to determine vocational fields in vocational and technical training in the most efficient manner. In this respect, it is possible to create a more efficient tool compared to the current methods by utilising machine learning which is an artificial intelligence model in energy applications that predicts events in the future depending on the past experiences. In the current study, a software is developed that ensures that the system learns about the successful and unsuccessful choices made in the past by applying “Naive Bayes” algorithm, which is a machine learning algorithm, to the data collected concerning the individuals who turned out to be successful or unsuccessful in the vocational technical training process in energy applications. In the software developed, it is aimed that the system recommends the most suitable vocational field for the individual by according to the data collected from the individual who is in the occupation selection process in field energy applications.
Keywords :
"Classification algorithms","Data mining","Prediction algorithms","Training data","Training","Machine learning algorithms","Probability"
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.109