Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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23 May 1998
TL;DR: It is proved that one help bit doesn’t help, and it is shown that f can be evaluated in depth d without any help bits, or else [log kJ + 1 help bits are required in order to evaluate k instances in Depth d.
Abstract: Nisan, Rudich, and Saks [25] a&cd whether one help bit can reduce the complexity of computing a boolean function on two inputs in the decision tree model. We prove that one help bit doesn’t help. In general, we prove that [log kJ help bits don’t help for computing k instances off. This result is the best possible, since we exhibit functions for which [log kJ + 1 help bits reduce complexity. This shows a gap between 0 and [log kJ + 1 help bits: either f can be evaluated in depth d (the complexity off) without any help bits, or else [log kJ + 1 help bits are required in order to evaluate k instances in depth d.
8 citations
TL;DR: The theory of communication complexity of individual inputs held by the agents is introduced in this paper, where the authors define the Kolmogorov complexity in terms of the complexity of the individual inputs.
8 citations
TL;DR: A novel approach to modeling realistic tree easily through interactive methods based on ball B-Spline Curves and an efficient graph based data structure of tree model is proposed in the paper.
Abstract: A novel approach to modeling realistic tree easily
through interactive methods based on ball B-Spline Curves
(BBSCs) and an efficient graph based data structure of tree model
is proposed in the paper. As BBSCs are flexible for modifying,
deforming and editing, these methods provide intuitive interaction
and more freedom for users to model trees. If conjuncted with
other methods like generating tree models through L-systems or
iterated function systems (IFS), the models are more realistic and
natural through modifying and editing. The method can be applied
to the design of bonsai tree models.
8 citations
01 Oct 2019
TL;DR: Through a large number of empirical data analysis, it is found the predictive accuracy of S-DT investment strategy is 89.5%, which is 11.6% higher than that of GRA-DT, ANN, DM and so on.
Abstract: Based on the decision tree model, we propose an S-DT investment strategy which predicts the closing price trends of international and domestic stocks. We train the history data of stocks through decision tree model. In order to make the stock price trend prediction accuracy is higher, we choose indexes that are highly correlated with closing price as input variables based on synergy factor, information entropy and grey association rules. Through a large number of empirical data analysis, we found the predictive accuracy of S-DT investment strategy is 89.5%, which is 11.6% higher than that of GRA-DT, ANN, DM and so on.
8 citations
01 Oct 2007
TL;DR: A new model for medical decision making situations is proposed that incorporates rule structures obtained from the classical ID3 approach of decision trees in predicting the degree of risks from the information obtained through clinical observations in coronary artery disease patients.
Abstract: The application of a neuro fuzzy model, typically a TSK model that incorporates rule structures obtained from the classical ID3 approach of decision trees in predicting the degree of risks from the information obtained through clinical observations in coronary artery disease patients is discussed in this paper. In recent years, numerous attempts have been made to use knowledge structures represented by fuzzy systems and artificial neural networks in various applications particularly in decision-making models. The utility of fuzzy systems lies in their ability for modeling uncertain or ambiguous, multi-parameter data often encountered in complex situations like medical diagnosis. This paper proposes a new model for medical decision making situations.
8 citations