Implementation of a classification algorithm for institutional analysis
Hongliang Sun · Open ULeth Scholarship (OPUS) (University of Lethbridge) · 2008
The report presents an implemention of a classification algorithm for the Institutional Anal-ysis Project. The algorithm used in this project is the decision tree classification algorithm which uses a gain ratio attribute selection method. The algorithm discovers the hidden rules from the student records, which are used to predict whether or not other students are at risk of dropping out. It is shown that special rules exist in different data sets, each with their natural hidden knowledge. In other words, the rules that are obtained depend on the data that is used for classification. In our preliminary experiments, we show that between 55-78 percent of data with unknown class lables can be correctly classified, using the rules ob-tained from data whose class labels are known. We feel this is acceptable, given the large number of records, attributes, and attribute values that are used in the experiments. The project results are useful for large data set analysis. iii Acknowledgements I thank all the people who helped me in my education at University of Lethbridge and gave