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» Lazy Learning for Improving Ranking of Decision Trees
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IFIP12
2008
15 years 1 months ago
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System
Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods ar...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
AAAI
1990
15 years 25 days ago
What Should Be Minimized in a Decision Tree?
In this paper, we address the issue of evaluating decision trees generated from training examples by a learning algorithm. We give a set of performance measures and show how some ...
Usama M. Fayyad, Keki B. Irani
ACL
2001
15 years 1 months ago
Japanese Named Entity Recognition based on a Simple Rule Generator and Decision Tree Learning
Named entity (NE) recognition is a task in which proper nouns and numerical information in a document are detected and classified into categories such as person, organization, loc...
Hideki Isozaki
ICML
2004
IEEE
16 years 14 days ago
Sequential skewing: an improved skewing algorithm
This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...
Soumya Ray, David Page
95
Voted
FLAIRS
2006
15 years 1 months ago
Generalized Entropy for Splitting on Numerical Attributes in Decision Trees
Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn...
Mingyu Zhong, Michael Georgiopoulos, Georgios C. A...