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APPROX
2008
Springer
245views Algorithms» more  APPROX 2008»
13 years 7 months ago
Approximating Optimal Binary Decision Trees
Abstract. We give a (ln n + 1)-approximation for the decision tree (DT) problem. An instance of DT is a set of m binary tests T = (T1, . . . , Tm) and a set of n items X = (X1, . ....
Micah Adler, Brent Heeringa
IPL
2010
114views more  IPL 2010»
13 years 3 months ago
Alphabetic coding with exponential costs
An alphabetic binary tree formulation applies to problems in which an outcome needs to be determined via alphabetically ordered search prior to the termination of some window of o...
Michael B. Baer
AAAI
2006
13 years 6 months ago
Cost-Sensitive Test Strategies
In medical diagnosis doctors must often determine what medical tests (e.g., X-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and mis...
Shengli Sheng, Charles X. Ling, Ailing Ni, Shichao...
AAAI
1990
13 years 6 months 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
ICML
1999
IEEE
14 years 6 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting