The Research and Analysis in Decision Tree Algorithm Based on C4.5 Algorithm

2018 ◽  
Author(s):  
Huan Hao ◽  
Tieming Chen ◽  
Jing Lu ◽  
Jie Liu ◽  
Xiaodan Ma
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm

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 Optimization of Management Mode of Small- and Medium-Sized Enterprises Based on Decision Tree Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuzhu Diao ◽  
Qing Zhang
Keyword(s):  
Project Management ◽  
Decision Tree ◽  
Performance Appraisal ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Management Information ◽  
Report Generation ◽  
Tree Algorithm ◽  
Gain Rate ◽  
C4.5 Algorithm

Decision tree algorithm is a common classification algorithm in data mining technology, and its results are usually expressed in the form of if-then rules. The C4.5 algorithm is one of the decision tree algorithms, which has the advantages of easy to understand and high accuracy, and the concept of information gain rate is added compared with its predecessor ID3 algorithm. After theoretical analysis, C4.5 algorithm is chosen to analyze the performance appraisal results, and the decision tree for performance appraisal is generated by collecting data, data preprocessing, calculating information gain rate, determining splitting attributes, and postpruning. The system is developed in B/S architecture, and an R&D project management system and platform that can realize performance assessment analysis are built by means of visualization tools, decision tree algorithm, and dynamic web pages. The system includes information storage, task management, report generation, role authority control, information visualization, and other management information system functional modules. They can realize the project management functions such as project establishment and management, task flow, employee information filling and management, performance assessment system establishment, report generation of various dimensions, management cockpit construction. With decision tree algorithm as the core technology, the system obtains scientific and reliable project management information with high accuracy and realizes data visualization, which can assist enterprises to establish a good management system in the era of big data.

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 METODE ALGORITMA DECISION TREE C4.5 UNTUK ANALISIS KELAYAKAN KREDIT NASABAH PADA BSM KCP KEMANG PRATAMA

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Dyah Wulandari ◽  
Nur Lutfiyana ◽  
Heny Sumarno
Keyword(s):  
Decision Tree ◽  
Profit Sharing ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Customer Data ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Credit Worthiness

Abstract - Credit is the provision of money or equivalent claims, based on agreements or agreements on loans between banks and other parties which require the borrowing party to repay the debt after a certain period of time with the amount of interest, compensation or profit sharing. From the credit customer data available at BSM KCP Kemang Pratama still has Non Performing Financing (NPF) or Bad Credit.In analyzing a credit sometimes an analyst does an inaccurate analysis, so there are some customers who are less able to make credit payments, resulting in bad credit. So the researchers conducted an analysis using the C4.5 decision tree algorithm and Rapid Miner application for determining credit worthiness. From the analysis of credit customer data using the C4.5 decision tree algorithm method, the feasibility of credit recipient customers is very effective and produces a value of accuracy on Rapid Miner 5.3 of 80%, Precision of 100% and Recall of 0% so as to minimize the risk.Keywords— Credit, C4.5 Algorithm, Rapid Miner, Value AccuracyAbstrak - Kredit merupakan penyediaan uang atau tagihan yang dapat disamakan dengan hal itu, berdasarkan persetujuan atau kesepakatan pinjaman-pinjaman antara bank dengan pihak lain yang mewajibkan pihak peminjam untuk melunasi utangnya setelah jangka waktu tertentu dengan jumlah bunga, imbalan atau pembagian hasil keuntungan. Dari data nasabah kredit yang ada pada BSM KCP Kemang Pratama masih memiliki Non Performing Financing (NPF) atau Kredit Macet. Dalam menganalisa sebuah kredit terkadang seorang analis melakukan analisa tidak akurat, sehingga ada beberapa nasabah yang kurang mampu dalam melakukan pembayaran kredit, dan pada akhirnya mengakibatkan kredit macet. Peneliti melakukan analisis menggunakan algoritma decision tree C4.5 dan aplikasi Rapid Miner untuk penentuan kelayakan pemberian kredit. Dari analisis data nasabah kredit menggunakan metode Algoritma decision tree C4.5 menghasilkan kelayakan nasabah penerima kredit sangat efektif dan menghasilkan nilai akurasi pada Rapid Miner 5.3 sebesar 80%, Precision sebesar 100% dan Recall sebesar 0% sehingga dapat meminimalisir resiko yang terjadi.Kata kunci— Kredit, Algoritma C4.5, Rapid Miner, Nilai Akurasi

The Application of Decision Tree Algorithm in the Employment Management System

2014 ◽  
Vol 543-547 ◽  
pp. 1639-1642 ◽  
Author(s):  
Liang Li ◽  
Ying Zheng ◽  
Xiao Hua Sun ◽  
Fu Shun Wang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Effective Means ◽  
University Graduates ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Decision Attributes ◽  
C4.5 Algorithm ◽  
Employment Data

According to students' employment problem, employment data mining model of university graduates is presented. The decision tree is very effective means for classification, which is proposed according to the characteristics of employment data and C4.5 algorithm. The C4.5 algorithm is improved from ID3 algorithm that is the core algorithm in the decision tree. The C4.5 algorithm is suitable for its simple construction, high processing speed and easy implementation. The model includes preprocess of the data of employment selection of decision attributes, implementation of mining algorithm, and obtainment of rules from the decision tree. The rules point out which decision attributes decide the classification of employers. Case study shows that the decision tree algorithm applied to employment information data mining, can classify data of employment correctly with simple structure and faster speed, and find some valuable results for analysis and decision. so the proposed algorithm in this paper is effective.

scholarly journals Analisa Tingkat Prestasi Atlet Karate Sumatera Barat Menggunakan Algoritma Data Mining

2018 ◽  
Vol 7 (2) ◽  
pp. 200-210
Author(s):  
Ronaldo Syahputra ◽  
Wifra Safitri
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Method ◽  
Sports Performance ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
West Sumatra ◽  
Karate Athletes

The Karate sport is a kind of sport that is quite popular today. All regions in Indonesia are racing to improve the performance of their karate athletes. Various developments were carried out to be able to improve karate sports achievements. This research will later be used as a benchmark for developing and realizing good sports performance, especially in the karate by using the concept of data mining. To apply the concept of Data Mining, one way that can be done is to implement the C4.5 algorithm. C4.5 algorithm or also called decision tree algorithm, is a very strong and well-known classification and prediction method. The application of the concept of data mining with C4.5 algorithm is done by analyzing what factors support the achievement of karate sports. After that, the C4.5 algorithm is calculated to find out what is the most decisive factor in the development of karate sports achievements. This aims to maximize the role of these achievement supporting factors. The results of this study are expected to provide great benefits for the development and improvement of the achievements of karate athletes in West Sumatra.

scholarly journals Penerapan Algoritma C4.5 Untuk Prediksi Loyalitas Nasabah PT Erdika Elit Jakarta

2020 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Khotibul Umam ◽  
Diah Puspitasari ◽  
Acmad Nurhadi
Keyword(s):  
Decision Tree ◽  
Customer Loyalty ◽  
Educational Background ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Transaction Data ◽  
Training Samples ◽  
C4.5 Algorithm ◽  
Influential Variable ◽  
Loyal Customers

C4.5 algorithm is a decision tree algorithm group. This algorithm has input in the form of training samples and samples. While samples are data fields which we will use as parameters in classifying data. From the variable transaction frequency the company can see which customers are loyal to the company based on historical customer transaction data, but there are still some variables that make customers loyal to the company. These variables are age, customer gender, company sales gender, educational background, customer  transaction frequency. The company knows how to predict customers who will be loyal to the company based on the experience of some of the variables above, but the company does not know the most influential variable in the assessment of loyal customers because of some of the variables above are not interconnected and it is uncertain if one variable can make a decision whether the customer loyal. Based on the decision tree that has made the most influential attribute on customer loyalty is the educational background because it has the highest gain value of 1.545292721 and as the root of the decision tree while the client's gender does not significantly affect customer loyalty because it is always at the last node with the gain value which is 0.623919119.

scholarly journals Research on Audit Data Analysis and Decision Tree Algorithm for Benefit Distribution of Enterprise Financing Alliance

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jie Bai ◽  
Tian He
Keyword(s):  
Data Analysis ◽  
Decision Tree ◽  
Data Cleaning ◽  
Analysis Method ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Average Accuracy ◽  
Audit Data ◽  
Data Collection Process ◽  
C4.5 Algorithm

When traditional methods analyze the audit data of enterprise financing alliance, there are some problems, such as long algorithm modeling time and low accuracy of interest distribution algorithm of enterprise financing alliance. Therefore, this paper proposes an analysis method of interest distribution of enterprise audit data financing alliance based on the decision tree algorithm. The audit data collection process of enterprise financing alliance is given, and the continuous attributes of audit data are discretized by the C4.5 algorithm. We perform enterprise financing alliance audit data analysis, remove inconsistencies from audit data through data cleaning, and finally realize enterprise financing alliance audit data analysis based on the improved C4.5 algorithm. The experimental results show that this method can shorten the modeling time and improve the accuracy of interest distribution algorithm of enterprise financing alliance. We achieved an average accuracy of 84.7% with the C4.5 algorithm while 84.35% with NBTree.

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