Implementation of a classification algorithm for institutional analysis
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Date
2008
Authors
Sun, Hongliang
University of Lethbridge. Faculty of Arts and Science
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2008
Abstract
The report presents an implemention of a classification algorithm for the Institutional Analysis
Project. The algorithm used in this project is the decision tree classification algorithm
which uses a gain ratio attribute selectionmethod. 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 obtained
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.
Description
viii, 38 leaves ; 29 cm. --
Keywords
Classification rule mining -- Alberta -- Lethbridge , Dropout behavior, Prediction of -- Analysis , Dropout behavior, Prediction of -- Computer programs , Pattern recognition systems , Data mining -- Alberta -- Lethbridge , Computer algorithms , College students -- Alberta -- Lethbridge