Predicate Selection for Structural Decision Trees

2005 ◽  
pp. 264-278
Author(s):  
K. S. Ng ◽  
J. W. Lloyd
Keyword(s):  
Decision Trees ◽  
Selection For

Fault zone identification and phase selection for microgrids using decision trees ensemble

2021 ◽  
Vol 132 ◽  
pp. 107178
Author(s):  
Khaled Saleh ◽  
Abdelrahman Ayad
Keyword(s):  
Decision Trees ◽  
Fault Zone ◽  
Phase Selection ◽  
Selection For

Effective customer selection for marketing campaigns based on net scores

2017 ◽  
Vol 11 (1) ◽  
pp. 2-15 ◽  
Author(s):  
René Michel ◽  
Igor Schnakenburg ◽  
Tobias von Martens
Keyword(s):  
Decision Trees ◽  
Direct Marketing ◽  
Real Data ◽  
Business Case ◽  
Added Value ◽  
Scoring Method ◽  
Content Type ◽  
Effective Selection ◽  
Selection For ◽  
Response Modeling

Purpose This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods. Design/methodology/approach First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches. Findings Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators. Practical implications The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures. Originality/value Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ2-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

scholarly journals When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 230 ◽  
Author(s):  
Izabela Karsznia ◽  
Karolina Sielicka
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Decision Trees ◽  
Selection Process ◽  
Small Scale ◽  
Map Generalization ◽  
Selection For

Effective settlements generalization for small-scale maps is a complex and challenging task. Developing a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we want to fill this gap and explore settlement characteristics, named variables that can be decisive in settlement selection for small-scale maps. We propose 33 variables, both thematic and topological, which may be of importance in the selection process. To find essential variables and assess their weights and correlations, we use machine learning (ML) models, especially decision trees (DT) and decision trees supported by genetic algorithms (DT-GA). With the use of ML models, we automatically classify settlements as selected and omitted. As a result, in each tested case, we achieve automatic settlement selection, an improvement in comparison with the selection based on official national mapping agency (NMA) guidelines and closer to the results obtained in manual map generalization conducted by experienced cartographers.

Feature selection for the naive bayesian classifier using decision trees

2003 ◽  
Vol 17 (5-6) ◽  
pp. 475-487 ◽  
Author(s):  
Chotirat "ann" Ratanamahatana ◽  
Dimitrios Gunopulos
Keyword(s):  
Feature Selection ◽  
Decision Trees ◽  
Bayesian Classifier ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naive Bayesian Classifier ◽  
Naïve Bayesian ◽  
Selection For

scholarly journals Integrally private model selection for decision trees

2019 ◽  
Vol 83 ◽  
pp. 167-181 ◽  
Author(s):  
Navoda Senavirathne ◽  
Vicenç Torra
Keyword(s):  
Model Selection ◽  
Decision Trees ◽  
Selection For

scholarly journals Feature selection for a rich HPSG grammar using decision trees

2002 ◽  
Author(s):  
Kristina Toutanova ◽  
Christopher D. Manning
Keyword(s):  
Feature Selection ◽  
Decision Trees ◽  
Selection For

scholarly journals Decision Trees Combined with Feature Selection for the Rational Synthesis of Aluminophosphate AlPO4-5

2011 ◽  
Vol 27 (09) ◽  
pp. 2111-2117 ◽  
Author(s):  
HUO Wei-Feng ◽  
◽  
GAO Na ◽  
YAN Yan ◽  
LI Ji-Yang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Decision Trees ◽  
Selection For

Application of Decision Trees for Candidate Well Selection for Geological and Technical Measures

2019 ◽  
Author(s):  
I. Gaydamak ◽  
O. Pichugin ◽  
S. Rodionov ◽  
S. Panarina
Keyword(s):  
Decision Trees ◽  
Selection For ◽  
Technical Measures
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