Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization

2013 ◽  
pp. 11-29 ◽  
Author(s):  
Abdulaziz Alkhalid ◽  
Igor Chikalov ◽  
Shahid Hussain ◽  
Mikhail Moshkov
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Tree Optimization

scholarly journals Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme

2001 ◽  
Vol 261 (1) ◽  
pp. 179-209 ◽  
Author(s):  
Eiji Takimoto ◽  
Akira Maruoka ◽  
Volodya Vovk
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Dynamic Programming Scheme

ODT: Optimal deadline-based trajectory for mobile sinks in WSN: A decision tree and dynamic programming approach

2015 ◽  
Vol 77 ◽  
pp. 128-143 ◽  
Author(s):  
Farzad Tashtarian ◽  
M.H. Yaghmaee Moghaddam ◽  
Khosrow Sohraby ◽  
Sohrab Effati
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Programming Approach ◽  
Dynamic Programming Approach ◽  
Mobile Sinks

A Pruning Method Based on Conditional Misclassification

2010 ◽  
Vol 44-47 ◽  
pp. 3448-3452
Author(s):  
Wei Xiang Xu ◽  
Jing Xu ◽  
Xu Min Liu ◽  
Rui Dong
Keyword(s):  
Decision Tree ◽  
Great Influence ◽  
Pruning Method ◽  
C4.5 Algorithm ◽  
Tree Optimization

The methods of pruning have great influence on the effect of the decision tree. By researching on the pruning method based on misclassification, introduced the conception of condition misclassification and improved the standard of pruning. Propose the conditional misclassification pruning method for decision tree optimization and apply it in C4.5 algorithm. The experiment result shows that the condition misclassification pruning can avoid over pruned problem and non-enough pruned problem to some extent and improve the accurate of classification.

Dynamic programming based fuzzy partition in fuzzy decision tree induction

2020 ◽  
Vol 39 (5) ◽  
pp. 6757-6772
Author(s):  
Yashuang Mu ◽  
Lidong Wang ◽  
Xiaodong Liu
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Decision Trees ◽  
Dynamic Programming Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Fuzzy Partition ◽  
Decision Tree Induction ◽  
Fuzzy Partitions ◽  
Fuzzy Decision Trees

Fuzzy decision trees are one of the most popular extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Among the majority of fuzzy decision trees learning methods, the number of fuzzy partitions is given in advance, that is, there are the same amount of fuzzy items utilized in each condition attribute. In this study, a dynamic programming-based partition criterion for fuzzy items is designed in the framework of fuzzy decision tree induction. The proposed criterion applies an improved dynamic programming algorithm used in scheduling problems to establish an optimal number of fuzzy items for each condition attribute. Then, based on these fuzzy partitions, a fuzzy decision tree is constructed in a top-down recursive way. A comparative analysis using several traditional decision trees verify the feasibility of the proposed dynamic programming based fuzzy partition criterion. Furthermore, under the same framework of fuzzy decision trees, the proposed fuzzy partition solution can obtain a higher classification accuracy than some cases with the same amount of fuzzy items.

Intelligent Questionnaires Using Approximate Dynamic Programming

i-com ◽  
2020 ◽  
Vol 19 (3) ◽  
pp. 227-237
Author(s):  
Frédéric Logé ◽  
Erwan Le Pennec ◽  
Habiboulaye Amadou-Boubacar
Keyword(s):  
Dynamic Programming ◽  
Decision Tree ◽  
Supervised Learning ◽  
Markov Decision Process ◽  
User Experience ◽  
Decision Process ◽  
Approximate Dynamic Programming ◽  
Budget Constraint ◽  
Markov Decision ◽  
The Right

Abstract Inefficient interaction such as long and/or repetitive questionnaires can be detrimental to user experience, which leads us to investigate the computation of an intelligent questionnaire for a prediction task. Given time and budget constraints (maximum q questions asked), this questionnaire will select adaptively the question sequence based on answers already given. Several use-cases with increased user and customer experience are given. The problem is framed as a Markov Decision Process and solved numerically with approximate dynamic programming, exploiting the hierarchical and episodic structure of the problem. The approach, evaluated on toy models and classic supervised learning datasets, outperforms two baselines: a decision tree with budget constraint and a model with q best features systematically asked. The online problem, quite critical for deployment seems to pose no particular issue, under the right exploration strategy. This setting is quite flexible and can incorporate easily initial available data and grouped questions.

scholarly journals Decision Tree Optimization in C4.5 Algorithm Using Genetic Algorithm

2019 ◽  
Vol 1255 ◽  
pp. 012012 ◽  
Author(s):  
Irfan Sudahri Damanik ◽  
Agus Perdana Windarto ◽  
Anjar Wanto ◽  
Poningsih ◽  
Sundari Retno Andani ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
C4.5 Algorithm ◽  
Tree Optimization
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