Parallel Implementation of Decision Tree Learning Algorithms
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
Making Knowledge Extraction and Reasoning Closer
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Shared Memory Parallelization of Decision Tree Construction Using a General Data Mining Middleware
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
An efficient data structure for decision rules discovery
Proceedings of the 2003 ACM symposium on Applied computing
IEEE Transactions on Knowledge and Data Engineering
Hybrid random subsample classifier ensemble for high dimensional data sets
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
We present an analytic evaluation of the run-time behavior of the C4.5 algorithm which highlights some efficiency improvements. We have implemented a more efficient version of the algorithm, called EC4.5, that improves on C4.5 by adopting the best among three strategies at each node construction. The first strategy uses a binary search of thresholds instead of the linear search of C4.5. The second strategy adopts a counting sort method instead of the quicksort of C4.5. The third strategy uses a main-memory version of the RainForest algorithm for constructing decision trees. Our implementation computes the same decision trees as C4.5 with a performance gain of up to 5 times.