2013/12/26 by Bangsuk Jantawan, Jantawan, Bangsuk, Cheng-Fa Tsai +1 · 1 citation
Computer Science · Health Professions · #Artificial Intelligence in Healthcare #Computers and Society (cs.CY) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #cs.CY
paper · pdf · doi:10.48550/arxiv.1312.7123
arxiv created 2013/12/26 · openalex publication_date 2013/12/26 · arxiv updated 2013/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The Tree method includes 5 algorithms, including BFTree, NBTree, REPTree, ID3, C4.5. The experiment uses a classification task in WEKA, and we compare the results of each algorithm, where several classification models were generated. To validate the generated model, the experiments were conducted using real data collected from graduate profile at the Maejo University in Thailand. The model is intended to be used for predicting whether a graduate was employed, unemployed, or in an undetermined situation.