Prediction of Potential Future IT Personnel in Bangladesh using Machine Learning Classifier
DOI:
https://doi.org/10.18034/gdeb.v6i1.112Keywords:
Future IT Personnel, Developing Country, Machine Learning ClassifierAbstract
Bangladesh is one of the most promising developing countries in IT sector, where people from several disciplines and experiences are involved in this sector. However, no direct analysis in this sector is published yet, which covers the proper guideline for predicting future IT personnel. Hence this is not a simple solution, training data from real IT sector are needed and trained several classifiers for detecting perfect results. Machine learning algorithms can be used for predicting future potential IT personnel. In this paper, four different classifiers named as Naive Bayes, J48, Bagging and Random Forest in five different folds are experimented for that prediction. Results are pointed out that Random Forest performs better accuracy than other experimented classifier for future IT personnel prediction. It is mentioned that the standard accuracy measurement process named as Precision, Recall, F-Measure, ROC Area etc. are used for evaluating the results.
Metrics
Downloads
References
Aditya Polumetla. Machine learning methods for the detection of RWIS sensor malfunctions. PhD thesis, Citeseer, 2006.
Alexis Marcano-Cedeno and Diego Andina. Data mining for the diagnosis of type 2 diabetes. In World Automation Congress (WAC), 2012, pages 1–6. IEEE, 2012.
Anantha M Prasad, Louis R Iverson, and Andy Liaw. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9(2):181–199, 2006.
Anna Bosch, Andrew Zisserman, and Xavier Munoz. Image classification using random forests and ferns. In 2007 IEEE 11th International Conference on Computer Vision, pages 1–8. IEEE, 2007. DOI: https://doi.org/10.1109/ICCV.2007.4409066
Asma A Al Jarullah. Decision tree discovery for the diagnosis of type ii diabetes. In Innovations in Information Technology (IIT), 2011 International Conference on, pages 303–307. IEEE, 2011. DOI: https://doi.org/10.1109/INNOVATIONS.2011.5893838
Charles X Ling and Chenghui Li. Data mining for direct marketing: Problems and solutions. In KDD, volume 98, pages 73–79, 1998.
Feixiang Huang, Shengyong Wang, and Chien-Chung Chan. Predicting disease by using data mining based on healthcare information system. In Granular Computing (GrC), 2012 IEEE International Conference on, pages 191–194. IEEE, 2012. DOI: https://doi.org/10.1109/GrC.2012.6468691
Gaganjot Kaur and Amit Chhabra. Improved j48 classification algorithm for the prediction of diabetes. International Journal of Computer Applications, 98(22), 2014 DOI: https://doi.org/10.5120/17314-7433
Gene M Ko, SA Reddy, Sunil Kumar, Barbara A Bailey, and Rajni Garg. A random forest model for the analysis of chemical descriptors for the elucidation of hiv1 protease protein–ligand interactions. Applied Computational Science and Engineering Student Support (ACSESS), San Diego State University, USA, 2010.
Kristína Machová, Frantisek Barcak, and Peter Bednár. A bagging method using decision trees in the role of base classifiers. Acta Polytechnica Hungarica, 3(2):121–132, 2006. DOI: https://doi.org/10.2298/CSIS0602057M
M Alam and SA Alam. Actionable knowledge mining from improved post processing decision trees. In International Conference on Computing and Control Engineering (ICCCE 2012), Chennai, pages 1–8, 2012.
Pat Langley and Stephanie Sage. Induction of selective bayesian classifiers. In Proceedings of the Tenth int. conference on Uncertainty in artificial intelligence, pages 399–406. Morgan Kaufmann Publishers Inc., 1994. DOI: https://doi.org/10.1016/B978-1-55860-332-5.50055-9
Qiang Yang, Jie Yin, Charles Ling, and Rong Pan. Extracting actionable knowledge from decision trees. IEEE Transactions on Knowledge and data Engineering, 19(1): 43–56, 2007. DOI: https://doi.org/10.1109/TKDE.2007.250584
Rahul A Patil, Prashant G Ahire, Pramod D Patil, and Avinash L Golande. Decision tree post processing for extraction of actionable knowledge. Int. Journal of Engineering and Innovative Technology, 2(1):152–55, 2012.
Robert Feldt and Ana Magazinius. Validity threats in empirical software engineering research-an initial survey. In SEKE, pages 374–379, 2010.
Varun Kumar and Nisha Rathee. Knowledge discovery from database using an integration of clustering and classification. International Journal of Advanced Computer Science and Applications, 2(3):29–33, 2011. DOI: https://doi.org/10.14569/IJACSA.2011.020306
Youvrajsinh Chauhan and Jignesh Vania. J48 classifier approach to detect characteristic of bt cotton base on soil micro nutrient. International Journal of Computer Trends and Technology (IJCTT), vol 5. 2014
Zengyou He, Xiaofei Xu, and Shengchun Deng. Data mining for actionable knowledge: A survey. arXiv preprint cs/0501079, 2005.
--0--
