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» Lazy Learning for Improving Ranking of Decision Trees
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IJPRAI
2002
93views more  IJPRAI 2002»
13 years 7 months ago
Improving Stability of Decision Trees
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
Mark Last, Oded Maimon, Einat Minkov
DASFAA
2005
IEEE
141views Database» more  DASFAA 2005»
14 years 1 months ago
Learning Tree Augmented Naive Bayes for Ranking
Naive Bayes has been widely used in data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, numerous algorit...
Liangxiao Jiang, Harry Zhang, Zhihua Cai, Jiang Su
ICML
1999
IEEE
14 years 8 months ago
The Alternating Decision Tree Learning Algorithm
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
Yoav Freund, Llew Mason
FLAIRS
2008
13 years 9 months ago
A Backward Adjusting Strategy and Optimization of the C4.5 Parameters to Improve C4.5's Performance
In machine learning, decision trees are employed extensively in solving classification problems. In order to design a decision tree classifier two main phases are employed. The fi...
Jason R. Beck, Maria Garcia, Mingyu Zhong, Michael...
KDD
2007
ACM
177views Data Mining» more  KDD 2007»
14 years 7 months ago
Mining optimal decision trees from itemset lattices
We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal...
Élisa Fromont, Siegfried Nijssen