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Scalable Decision Tree SPRINT

Project Members Kaushal Mittal Abhishek Seth Amar Agrawal. Scalable Decision Tree SPRINT. Problem Statement. 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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Scalable Decision Tree SPRINT

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  1. Project Members Kaushal Mittal Abhishek Seth Amar Agrawal Scalable Decision Tree SPRINT

  2. Problem Statement • Current decision tree implementation in Weka fails for large datasets. • Scalable implementation of decision trees in Weka. • Support for disk resident data.

  3. Challenges • 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 memory-resident instance data.

  4. Changes in Weka • 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.

  5. Decision Tree Classifier • Design similar to Weka classifier J48. • SPRINT algorithm implemented. • Use of disk resident attribute lists. • Generates a binary classifier tree. • Uses Gini index as split criteria.

  6. Results • Accuracy comparable to J48. • Glass 214 • J48 - 100% • Sprint – 91.667% • Adult • J48 – 83.3% • Sprint – 79.8 % • Execution time – More than default J48 for small data sets(IO). For large data sets, Weka fails.

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