scholarly journals Classification of Loan Applications of Garima Bikas Bank Ltd Using Decision Tree Classification Method

2019 ◽  
Vol 5 ◽  
pp. 147-152
Author(s):  
Subik Shrestha ◽  
Laxman Paudel
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Application Process ◽  
Tree Model ◽  
Loan Repayment ◽  
Decision Tree Classification ◽  
Attribute Information ◽  
The Status ◽  
Hidden Patterns

There is a possibility in finding hidden patterns that might help find a relationship between the information provided by the Loan Applicants during the Loan Application process and the status of their loan repayment. This paper highlights on finding such patterns by building a Decision Tree with the help of the data provided during the loan application process. Eleven attribute information of Five Hundred sixty four loan applicants were collected from Garima Bikas Bank Ltd. A decision tree model with a depth of 6 has been built by calculating the entropy and information gain at each split and selecting the feature with the highest information gain.

scholarly journals АЛГОРИТМ КЛАСИФІКАЦІЇ ПОЛІТРАВМ МЕТОДОМ ІНДУКЦІЇ ДЕРЕВА РІШЕНЬ

2013 ◽  
Author(s):  
R. M. Borys ◽  
V. P. Martsenyuk
Keyword(s):  
Decision Tree ◽  
Biochemical Parameters ◽  
Information Gain ◽  
Selection Algorithm ◽  
Work Program ◽  
Attribute Information ◽  
Decision Tree Method ◽  
Tree Method

<p class="a0">The work program is developed and implemented induction decision tree method for classification of polytrauma based on a number of biochemical parameters.</p><p class="a0">The selection algorithm     uses the value of the attribute information gain. The project was implemented in the  medium</p><p class="a0">Netbeans Java-based classes.</p>

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

scholarly journals Automated Decision Tree Classification of Corneal Shape

2005 ◽  
Vol 82 (12) ◽  
pp. 1038-1046 ◽  
Author(s):  
MICHAEL D. TWA ◽  
SRINIVASAN PARTHASARATHY ◽  
CYNTHIA ROBERTS ◽  
ASHRAF M. MAHMOUD ◽  
THOMAS W. RAASCH ◽  
...  
Keyword(s):  
Decision Tree ◽  
Decision Tree Classification ◽  
Corneal Shape
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