Theoretical Construction of the Analysis Model of College English Needs Based on Decision Tree Technology

2021 ◽  
Author(s):  
Fen Xie
Keyword(s):  
Decision Tree ◽  
Analysis Model ◽  
College English ◽  
Theoretical Construction

Research of Bid Evaluation Risk Analysis Model Based on Decision Tree

2014 ◽  
Vol 998-999 ◽  
pp. 1595-1600
Author(s):  
Hui Kai Gao ◽  
Lin Ying Xu ◽  
Cai Hong Li
Keyword(s):  
Risk Factors ◽  
Statistical Theory ◽  
Risk Analysis ◽  
Decision Tree ◽  
Project Implementation ◽  
Classification Rules ◽  
Analysis Model ◽  
C4.5 Algorithm ◽  
Potential Risks ◽  
Bid Evaluation

Human and objective factors in bid evaluation might bring some risks to the relevant project. Therefore, an evaluation results-oriented model is developed for analyzing the projects' risks. The risk analysis model analyzes bidders’ evaluation results data by adopting the C4.5 algorithm, and then conducts risk analysis based on statistical theory and classification rules of the decision tree. The experiment result shows that the model could correctly detect risk factors of the bid-winning enterprise, issue early warnings of the potential risks during the project implementation, and provide suggestions to cope with the risks.

scholarly journals Pemodelan Identifikasi Trafik Bittorrent Dengan Pendekatan Correlation Based Feature Selection (CFS) Menggunakan Algoritme Decision Tree (C4.5)

2021 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Hesmi Aria Yanti ◽  
Heru Sukoco ◽  
Shelvie Nidya Neyman
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Network Performance ◽  
Secondary Data ◽  
Training Model ◽  
Training Data ◽  
Traffic Data ◽  
Analysis Model ◽  
Traffic Identification ◽  
Correlation Based Feature Selection

Abstract— BitTorrent is a P2P file sharing software protocol that allows clients to apply data to other clients and can affect network performance. Bittorent client traffic data collection uses secondary data taken from official sources on the link https://unb.ca/cic/datasets/index.html in 2016. Traffic data is used as a model for BitTorrent traffic identification using feature-based correlation selection (CFS) and traffic analysis model analysis using Decision Tree Algorithm (C4.5). Feature selection is done to clean irrelevant features so that they can affect the results of the accuracy value. The results of feature selection obtained 7 features and 1 category with 244,689 records and the system connecting the rule tree data training model selected the four best accuracy values. Furthermore, the model training data is carried out by testing the BitTorrent traffic trial data. The results of data testing obtained the best BitTorrent traffic accuracy value of 98.82% with 73,406 records on the 30% data test. Keywords— BitTorrent, C4.5 algorithm, correlation based feature selection, traffic identification, modeling.

scholarly journals Analysis on Influencing Factors of Dance Teaching Effect in Colleges Based on Data Analysis and Decision Tree Model

2020 ◽  
Vol 15 (09) ◽  
pp. 245 ◽  
Author(s):  
Beibei Guo
Keyword(s):  
Data Analysis ◽  
Decision Tree ◽  
Influencing Factors ◽  
Decision Tree Model ◽  
Analysis Model ◽  
Tree Model ◽  
Factors Affecting ◽  
New Era ◽  
Research Findings ◽  
Set Up

In colleges, dance teaching is influenced by a variety of factors. It is very difficult to clarify how much each factor impacts the teaching effect. To overcome the difficulty, this paper explores the factors affecting the dance teaching effect in colleges based on data analysis and decision tree model. Firstly, the authors enumerated the goals of dance teaching for college students in the new era, and then summed up the constraints on the influencing factors of dance teaching effect in colleges. On this basis, an analysis model was established for the influencing factors, while the corresponding extensible decision tree was set up and verified through example analysis. The research findings shed new light on the theories of dance teaching in colleges, and provide an analysis model with great application potential.

scholarly journals SISTEM PENUNJANG KEPUTUSAN PEMILIHAN BEASISWA DENGAN METODE DECISION TRE ID3 PADA SMAN 2 CIBINONG KABUPATEN BOGOR

2019 ◽  
Vol 7 (01) ◽  
pp. 38
Author(s):  
Linda Sukmawati
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Comparative Test ◽  
Analysis Model ◽  
Academic Values ◽  
Test Analysis ◽  
Selection Of

The selection of scholarships must be in accordance with established rules. The criteria set out in this case study are academic values, parents' income, number of siblings, number of dependents of parents, active organizations and others. Therefore not all who register as candidates for the scholarship will be accepted, only those who meet the criteria will get the scholarship. Because of the large number of participants who apply for scholarships, it is necessary to build a decision support system that will help determine who is entitled to get the scholarship. Therefore, this study seeks to provide a solution for the selection of students who deserve a scholarship in accordance with the criteria that have been determined by each to produce more accurate and faster data. Decision Tree ID3 method as a comparative test analysis model, as well as policy analysis is used to support appropriate decision making for the school board. ID3 Decision Tree is suitable for use in this case because it can make data classification accuracy and tree generated results are very easy to read by humans.

scholarly journals Intelligent Medical Auxiliary Diagnosis Algorithm Based on Improved Decision Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yuntao Wei ◽  
Xiaojuan Wang ◽  
Meishan Li
Keyword(s):  
Decision Tree ◽  
Classification Performance ◽  
Optimal Decision ◽  
Analysis Method ◽  
Analysis Model ◽  
Tree Model ◽  
Pathological Characteristics ◽  
Adaptive Classification ◽  
Dependent Variables ◽  
Physiological Indexes

In order to address the problem of low ability of intelligent medical auxiliary diagnosis (IMAD), an IMAD based on improved decision tree is proposed. Firstly, the constraint parameter model of IMAD is constructed. Secondly, according to the physiological indexes of IMAD, the independent variables and dependent variables of auxiliary diagnosis are constructed, the quantitative recurrent analysis of IMAD is carried out by using regression analysis method, the data analysis model of IMAD is constructed, and the adaptive classification and recognition of IMAD are carried out. Finally, the attribute feature quantity of IMAD with pathological characteristics is extracted, and the improved decision tree model is used to realize intelligent medical auxiliary, assist in the optimal decision of diagnosis, and realize the effective classification and recognition of pathological characteristics. The results show that this method has better decision-making ability and better classification performance for IMAD, which improves the intelligence and accuracy of intelligent medical auxiliary diagnosis.

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