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Scalable Decision Tree
SPRINT
Project Members
Kaushal Mittal
Abhishek Seth
Amar Agrawal
Problem Statement
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Current decision tree implementation in Weka
fails for large datasets.
Scalable implementation of decision trees in
Weka.
Support for disk resident data.
Challenges
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Instance class in Weka loads the entire training
data in memory.
Multiple copies of the instance data made at
several points during the training.
Other classes assume the existence of memoryresident instance data.
Changes in Weka
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Extended the Instance class to support disk
resident data.
Use of cache and random access files.
Changes to the Evaluation class to work with the
new SInstance class.
Decision Tree Classifier
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Design similar to Weka classifier J48.
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SPRINT algorithm implemented.
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Use of disk resident attribute lists.
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Generates a binary classifier tree.
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Uses Gini index as split criteria.
Results
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Accuracy comparable to J48.
Glass 214
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J48 - 100%
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Sprint – 91.667%
Adult
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J48 – 83.3%
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Sprint – 79.8 %
Execution time – More than default J48 for small
data sets(IO). For large data sets, Weka fails.