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. / viii, 38 leaves ; 29 cm. --
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:ALU.w.uleth.ca/dspace#10133/738 |
| Date | January 2008 |
| Creators | Sun, Hongliang, University of Lethbridge. Faculty of Arts and Science |
| Contributors | Osborn, Wendy, Fiske, Jo-Anne |
| Publisher | Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2008, Arts and Science, Mathematics and Computer Science |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Relation | Project (University of Lethbridge. Faculty of Arts and Science) |
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