Online Submission!

Open Journal Systems


Tahseen A. Wotaifi, Eman S. Al-Shamery


With the great change in the labor market and contemporary education, a high consideration is paid for the issue of graduates’ employability and their earning. However, educational institutes and universities have different polices for preparing graduates to compete strongly in the labor market based on their academic skills and experience. This paper aims at (1) predicting the earnings of alumni for six years after graduation, and (2) identifying the most important factors that directly affect the earnings. Hence, the accuracy of the prediction can be improved based on the higher education system in the United States. Unlike previous research, this study contributes to the use of fuzzy logic on three filter methods: Relief Attribute Evaluation, Correlation Attribute Evaluation, and classifier Attribute Evaluation. Because these three methods provide different weight to the same attribute, the fuzzy logic is used to obtain a single weight. The proposed system is applied on higher education of United States; specifically the college scorecard data set that contains nearly (8000) educational institutions and (1825) feature for each. The proposed Fuzzy-Filter method has selected 45 features only. Accordingly, two models are used to predict graduates' earnings, which are Random Forest and Linear Regression with Mean Absolute Error (MAE) (0.055) and (0.065) respectively. The research findings are better in terms of Mean Absolute Error (MAE) and reducing the number of features in comparison to previous studies.


Earning prediction of Alumni; Fuzzy-Filter Feature Selection; Linear Regression; Mining Higher Education; Random Forest.

Full Text:



Monica Agrawal, PriyaGanesan, Keith Wyngarden.Prediction of Post-Collegiate Earnings and Debt, (2017).

Miranda Strand, Tommy Truong.Predicting Student Earnings After College, (2016).

Bhise R.B., Thorat S.S., Supekar A.K. Importance of Data Mining in Higher Education System, (2013).

Ewan Wright, QiangHao, Khaled Rasheed, & Yan Liu. Feature Selection of Post-Graduation Income of College Students in the United States, (2018).

Brijesh Kumar Baradwaj, Saurabh Pal. Mining Educational Data to Analyze Students‟ Performance, (2011).

M.Kavitha and , and , P.T.Srinivasan, G. Renuga and L.V.Jayakumar. "Evaluation of Antimitotic Activity of Mukiamaderaspatana L. Leaf Extract in Allium cepa Root Model." International Journal of Pharmacy Research & Technology 4 (2014), 01-04.

Peter D. Eckel, Jacqueline E. King .an overview of higher education in united states, (2005).

A. Jović, K. Brkić,and N. Bogunović. A review of feature selection methods with applications, (2014).

Mark A. Hall. Feature Selection for Discrete and Numeric Class Machine Learning, (1998).

HarveyMotulsky, Arthur Christopoulos. Fitting Models to Biological Data Using Linear and Nonlinear Regression, (2002).

Anne-Laure Boulesteix, SilkeJanitza, JochenKruppa, and Inke R. König. Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics, (2012).

Bin Luo, Qi Zhang, Somya D. Mohanty. Data-Driven Exploration of Factors Affecting Federal Student Loan Repayment, (2018).

Michael T. French, Jenny F. Homer, IoanaPopovici, and Philip K. Robins. What You Do in High School Matters: High School GPA, Educational Attainment, and Labor Market Earnings as a Young Adult, (2015).

Eman Al-Shamery and Ameer Al-Haq Al-Shamery. A New Deep Neural Network Regression Predictor Based Stock Market, (2018)

J., Yakubu H., and AboiyarT.. "A chaos based image encryption algorithm using Shimizu- Morioka system." International Journal of Communication and Computer Technologies 6 (2018), 7-11.

Eman Al-Shamery and Ameer Al-Haq Al-Shamery. Enhancing Prediction of NASDAQ Stock Market Based on Technical Indicators, (2018).



  • There are currently no refbacks.