An Improved Synthesized Decision Tree Algorithm and its Application

2010 ◽  
Vol 108-111 ◽  
pp. 244-249
Author(s):  
Jian Lin Qiu ◽  
Dan Ji ◽  
Xiang Gu ◽  
Fen Li ◽  
Peng He
Keyword(s):  
Decision Tree ◽  
Dimension Reduction ◽  
Small Scale ◽  
Decision Tree Algorithm ◽  
Clustering Methods ◽  
Tree Algorithm ◽  
Initial Dataset ◽  
Tree Methods ◽  
Decision Tree Method ◽  
Tree Method

Decision tree classification is one of the most widely-used methods in data mining which can provide useful decision-making analysis for users. But most of the decision tree methods have some efficiency bottle-necks and can only applied to small-scale datasets. In this paper, we present an new improved synthesized decision tree algorithm named CA which includes three important parts like dimension reduction, pre-clustering and decision tree method, and also give out its formalized specification. Through dimension reduction and synthesized pre-clustering methods, we can optimize the initial dataset and considerably reduce the decision tree’s input computation costs. We also improve the decision tree method by introducing parallel processing concept which can enhance its calculation precision and decision efficiency. This paper applies CA into maize seed breeding and analyzes its efficiency in every part comparing with original methods, and the results shows that CA algorithm is better.

The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Construction Engineering ◽  
Improvement Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Selection Decision ◽  
Decision Tree Method ◽  
Result Analysis ◽  
Tree Method

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.

scholarly journals Identifikasi Tingkat Kerusakan Peralatan Labor Teknik Komputer Jaringan Menggunakan Metode Decision Tree

2021 ◽  
pp. 275-280
Author(s):  
Dinda Permata Sukma ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Decision Tree ◽  
Teaching And Learning ◽  
Computer Network ◽  
Computer Engineering ◽  
Decision Tree Algorithm ◽  
Laboratory Equipment ◽  
Tree Algorithm ◽  
Computer Laboratory ◽  
Network Computer ◽  
Decision Tree Method

The computer laboratory is a place for practical learning for students, where computers have an important role in the smooth running of the practice. The use of computer labor at any time is very vulnerable to damage. If there is damage it will disrupt the teaching and learning process. Utilization of data mining in determining the level of damage is one of them. SMKN 1 Sintuk Toboh Gadang has 3 laboratories, TKJ (Network Computer Engineering), RPL (Software Engineering) and Technician labor. Application of the Decision Tree method in identifying damage to computer laboratory equipment, especially TKJ (Computer Network Engineering) labor. The data obtained in this study are computer equipment sourced from the computer laboratory of SMKN 1 Sintuk Toboh Gadang. Based on the analysis of the computer laboratory, there are 50 computer laboratory equipment. Furthermore, if the data is processed, several variables are needed to identify the level of damage to labor equipment including the name of the tool, number of tools, inspection, duration of use, and condition. The result of testing this method is to test whether the labor equipment can still be used or repaired. The purpose of this research is to help computer labor technicians to identify computer labor equipment that can still be used or repaired so that no damage occurs during practical learning hours. Furthermore, the best method in determining the level of damage to computer laboratory equipment is the Decision Tree Algorithm method. Decision Tree Algorithm is a predictive model using a decision tree structure and makes complex decisions simpler. The results of the research method show that the condition variable has the highest Gain value, namely 0.4734353, then the variable length of use is obtained with a Gain value of 0.896038. The factors that cause damage include the condition of the tool and the duration of use.

Study on Data Mining with Decision Tree Algorithm in the Student Information Management System

2014 ◽  
Vol 543-547 ◽  
pp. 3602-3605
Author(s):  
Liang Li ◽  
Ying Zheng ◽  
Xiao Hua Sun ◽  
Fu Shun Wang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Information Management ◽  
Management System ◽  
Information Management System ◽  
Decision Tree Algorithm ◽  
Mining Technology ◽  
Tree Algorithm ◽  
Student Information ◽  
Decision Tree Method

With the advance of education informationization in china, information technology and data mining technology has been widely used in the field of education. The decision tree method is one of the data mining methods; it does not require any assumption, can intelligent classification to a large amount of data directly. According to certain rules to find hidden and valuable information.Use the idea of learners as the center as guiding principle, using decision tree algorithm of data mining technology to build student information management system, selects typical C4.5 algorithm among the decision tree methods, Take mass information about employment of graduation in university students' information management system as an example to generate the decision tree, collect potential rules and factors in favor of graduat employment, so as to guide the education and management.

scholarly journals Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method

2019 ◽  
Vol 2 (1) ◽  
pp. 45
Author(s):  
Iqbal Dzulfiqar Iskandar
Keyword(s):  
Decision Tree ◽  
Business Intelligence ◽  
Decision Tree Algorithm ◽  
Tuition Fees ◽  
Public And Private ◽  
Operational Costs ◽  
Decision Tree Method ◽  
Intelligence Approach ◽  
Tree Method ◽  
Tuition Fee

School tuition fee is typically used for funding school operational, i.e. paying honorary teachers in public and private schools, purchasing practical instruments, printing examination worksheets, and other net-operational costs. According to the discovered data in the research environment, the funding is unable to be acquired properly due to students’ school tuition fees arrears for months even years until they graduate. Considering the condition, this research is conducted to identify the potential of students’ school tuition arrears, based on the sum of their parents’ salaries centered on the business intelligence approach, using the decision tree method. The analysis results show that, students whose parents’ income is less than Rp 672.500,00 will be potentially in arrears with school tuition more than  Rp 900.000,00 each month, while students whose parents’ income is above Rp 672.500,00 are potentially in arrears of less than Rp 900.000,00 or not in arrears. To evaluate the effectiveness of the decision tree algorithm for data processing, it has an accuracy value of 95.97%, with a precision of 94.96% that means the algorithm has a good correlation based on attributes and the data that have been processed by the algorithm.

scholarly journals PENDEKATAN METODE POHON KEPUTUSAN MENGGUNAKAN ALGORITMA ITERATIVE DICHOTOMIZER (ID3) UNTUK PENGUKURAN KINERJA PEGAWAI NEGERI SIPIL

SAINTEKBU ◽  
2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoseph Pius Kurniawan Kelen ◽  
Yohanis Ndapa Deda
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Employee Performance ◽  
Objective Assessment ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Training Samples ◽  
Tree Methods ◽  
Decision Tree Method ◽  
Tree Method

Decision tree method is a classification method that has been widely used for the solution of problems of classification. Decision tree classification provides a rapid and effective method. The approach has been proven decision tree method can be applied in various fields of life. Capability classification is indicated by the decision tree method is what encourages authors to use decision tree methods approach to measure the performance of civil servants.  To build a decision tree induction algorithms used. In this study, the ID3 algorithm method is used to construct a decision tree. Starting with the data collecting training samples and then measuring the entropy and information gain. Information Gain value will be used as the root of a decision tree. And translates it into a decision tree classification rules.The results show that the decision tree method is used to produce classification rules into groups employee performance Good and Bad. The resulting rules are used to measure the performance of employees and classifying employees into two groups.The result to assist management in making more objective assessment process. Keywords: ID3 Algorithm, Decision Tree, Employee Performance.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

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