Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Factors Associated With Persistence in Science and Engineering Majors: An Exploratory Study Using Classification Trees and Random Forests

(2013) Guillermo Mendez et al.   JOURNAL OF ENGINEERING EDUCATION

The use of genetic programming for the construction of a financial management model in an enterprise

(2010) Wen-Tsao Pan   APPLIED INTELLIGENCE

Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters

(2010) Laura Dioşan et al.   APPLIED INTELLIGENCE

Educational Data Mining: A Review of the State of the Art

(2010) Cristóbal Romero et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE

A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education

(2010) S. Kotsiantis et al.   KNOWLEDGE-BASED SYSTEMS

Factors influencing university drop out rates

(2009) Francisco Araque et al.   COMPUTERS & EDUCATION

Dropout prediction in e-learning courses through the combination of machine learning techniques

(2009) Ioanna Lykourentzou et al.   COMPUTERS & EDUCATION

Two decades of Ripple Down Rules research

(2009) Debbie Richards   KNOWLEDGE ENGINEERING REVIEW

Predicting who will drop out of nursing courses: A machine learning exercise

(2007) Laurence G. Moseley et al.   NURSE EDUCATION TODAY