On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
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ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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Just enough learning (of association rules): the TAR2 "Treatment" learner
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Expert Systems with Applications: An International Journal
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Hi-index | 12.05 |
Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family's social-economic status, high school GPA and test scores.