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| Zhi-Hua Zhou, Yuan Jiang, "NeC4.5: Neural Ensemble Based C4.5," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 770-773, June, 2004. | |||
| BibTex | x | ||
| @article{ 10.1109/TKDE.2004.11, author = {Zhi-Hua Zhou and Yuan Jiang}, title = {NeC4.5: Neural Ensemble Based C4.5}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {16}, number = {6}, issn = {1041-4347}, year = {2004}, pages = {770-773}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.11}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - NeC4.5: Neural Ensemble Based C4.5 IS - 6 SN - 1041-4347 SP770 EP773 EPD - 770-773 A1 - Zhi-Hua Zhou, A1 - Yuan Jiang, PY - 2004 KW - Machine learning KW - decision tree KW - neural networks KW - ensemble learning KW - neural network ensemble KW - generalization KW - comprehensibility. VL - 16 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
Abstract—Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability. In this paper, these merits are integrated into a novel decision tree algorithm NeC4.5. This algorithm trains a neural network ensemble at first. Then, the trained ensemble is employed to generate a new training set through replacing the desired class labels of the original training examples with those output from the trained ensemble. Some extra training examples are also generated from the trained ensemble and added to the new training set. Finally, a C4.5 decision tree is grown from the new training set. Since its learning results are decision trees, the comprehensibility of NeC4.5 is better than that of neural network ensemble. Moreover, experiments show that the generalization ability of NeC4.5 decision trees can be better than that of C4.5 decision trees.
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