scholarly journals Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm

2018 ◽  
Vol 13 (10) ◽  
pp. 146 ◽  
Author(s):  
Sujuan Jia ◽  
Yajing Pang
Keyword(s):  
Decision Tree ◽  
Quality Evaluation ◽  
Computation Time ◽  
Teaching Quality ◽  
Quality Data ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Tree Algorithm ◽  
Higher Education System ◽  
C4.5 Decision Tree

Vast data in the higher education system are used to analyse and evaluate the teaching quality, so that the key factors that affect the quality of teaching can be predicted. Besides, the learner’s personalized behaviour can also become the data source for teaching result prediction. This paper proposes a decision tree model by taking the teaching quality data and the statistical analysis results of the learn-er’s personalized behaviour as inputs. This model was based on the improved C4.5 decision tree algorithm, which used the FAYYAD boundary point decision theorem for effectively reducing the computation time to the most threshold. In this algorithm, the iterative analysis mechanism was introduced in combination with the data change of the learner’s personalized behaviour, so as to dynamically adjust the final teaching evaluation result. Finally, according to the actual statisti-cal data of one academic year, the teaching quality evaluation was effectively completed and the direction of future teaching prediction was proposed.

The Analysis and Application of the C4.5 Algorithm in Decision Tree Technology

2012 ◽  
Vol 457-458 ◽  
pp. 754-757
Author(s):  
Hong Yan Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Teaching Quality ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Result Analysis

The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree’s concept, the C4.5 Algorithm and the construction of The Decision Tree, the using of C4.5 Decision Tree Algorithm was applied to result analysis of students’ score for the purpose of improving the teaching quality.

scholarly journals C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

2020 ◽  
Vol 27 (3) ◽  
pp. 29-43
Author(s):  
Sihem Oujdi ◽  
Hafida Belbachir ◽  
Faouzi Boufares
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Spatial Data ◽  
Computation Time ◽  
Spatial Relations ◽  
Spatial Data Analysis ◽  
Data Organization ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2296-2300 ◽  
Author(s):  
Fei Shuai ◽  
Jun Quan Li
Keyword(s):  
Computational Complexity ◽  
Information Security ◽  
Decision Tree ◽  
Recognition Algorithm ◽  
Space Complexity ◽  
Complex Relationship ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

In current, there are complex relationship between the assets of information security product. According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C4.5 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm. Finally, we demonstrate that our algorithm is more precise than C4.5 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm.Keywordsdecision tree, information security product, asset recognition, C4.5

scholarly journals C4.5 DECISION TREE IMPLEMENTATION IN SISTEM INFORMASI ZAKAT (SIZAKAT) TO AUTOMATICALLY DETERMINING THE AMOUNT OF ZAKAT RECEIVED BY MUSTAHIK

2014 ◽  
Vol 10 (1) ◽  
pp. 28 ◽  
Author(s):  
David Bayu Ananda ◽  
Ari Wibisono
Keyword(s):  
Information Systems ◽  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Accuracy Rate ◽  
Tree Algorithm ◽  
C4.5 Decision Tree

Abstract In general, Zakat Information Systems is established to manage the zakat services, so that the data can be well documented. This study proposes the existence of a feature that will determine the amount of zakat received by Mustahik automatically using C4.5 Decision Tree algorithm. This feature is expected to make the process of determining the amount of zakat be done easy and optimal. The data used in this study are the data taken from Masjid An-Nur, Pancoran, South Jakarta. The experiment results show that the proposed feature produces an accuracy rate over 85%.

Sea-Battlefield Situation Assessment Based on Improved Decision Tree

2014 ◽  
Vol 574 ◽  
pp. 639-645
Author(s):  
Yong Tao Yu ◽  
Ying Ding
Keyword(s):  
Decision Tree ◽  
Decision Tree Model ◽  
Decision Tree Algorithm ◽  
Tree Model ◽  
Situation Assessment ◽  
Tree Algorithm ◽  
Initial Decision

How to efficiently evaluate the dynamic changing sea-battlefield is the key of command decision. According to research sea-battlefield situation assessment based on improved decision tree algorithm based on derived attributes. First is based on the decision tree algorithm to establish the sea-battlefield situation assessment initial decision tree. Second are dynamically generated derivative branches set based on derived attributes. Once again, it can be grafting and pruning derivative branches to form the sea-battlefield situation assessment derived decision tree model. Finally, it may use a derived dynamically decision tree model assess sea-battlefield in different time slices.

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