Optimization of Decision Rules Based on Dynamic Programming Approach

2013 ◽  
pp. 369-392 ◽  
Author(s):  
Beata Zielosko ◽  
Igor Chikalov ◽  
Mikhail Moshkov ◽  
Talha Amin
Keyword(s):  
Dynamic Programming ◽  
Decision Rules ◽  
Programming Approach ◽  
Dynamic Programming Approach

scholarly journals Decision Rules Construction: Algorithm Based on EAV Model

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 14
Author(s):  
Krzysztof Żabiński ◽  
Beata Zielosko
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Standard Deviation ◽  
Decision Rules ◽  
Point Of View ◽  
Supervised Machine Learning ◽  
Decision Table ◽  
Programming Approach ◽  
Classification Error ◽  
Dynamic Programming Approach

In the paper, an approach for decision rules construction is proposed. It is studied from the point of view of the supervised machine learning task, i.e., classification, and from the point of view of knowledge representation. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, the proposed algorithm is based on transformation of decision table into entity–attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’ values of attributes in particular decision classes was introduced. It allows to select from the whole set of attributes only these which provide the highest degree of information about the decision. Construction of decision rules is performed based on idea of partitioning of a decision table into corresponding subtables. In opposite to dynamic programming approach, not all attributes need to be taken into account but only these with the highest values of standard deviation per decision classes. Consequently, the proposed solution is more time efficient because of lower computational complexity. In the framework of experimental results, support and length of decision rules were computed and compared with the values of optimal rules. The classification error for data sets from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic programming approach. Performed experiments show that constructed rules are not far from the optimal ones and classification results are comparable to these obtained in the framework of the dynamic programming extension.

Dynamic programming approach to optimization of approximate decision rules

2013 ◽  
Vol 221 ◽  
pp. 403-418 ◽  
Author(s):  
Talha Amin ◽  
Igor Chikalov ◽  
Mikhail Moshkov ◽  
Beata Zielosko
Keyword(s):  
Dynamic Programming ◽  
Decision Rules ◽  
Programming Approach ◽  
Dynamic Programming Approach ◽  
Approximate Decision

The effects of feed and energy costs on broiler farm decisions: A dynamic programming approach

2021 ◽  
Author(s):  
Samantha L. Padilla ◽  
Lenis Saweda O. Liverpool‐Tasie ◽  
Robert J. Myers
Keyword(s):  
Dynamic Programming ◽  
Programming Approach ◽  
Energy Costs ◽  
Dynamic Programming Approach ◽  
Broiler Farm

A Dynamic Programming Approach to Optimal Lane Merging of Connected and Autonomous Vehicles

2020 ◽  
Author(s):  
Shang-Chien Lin ◽  
Hsiang Hsu ◽  
Yi-Ting Lin ◽  
Chung-Wei Lin ◽  
Iris Hui-Ru Jiang ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Autonomous Vehicles ◽  
Programming Approach ◽  
Dynamic Programming Approach
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