An Improved ID3 Decision Tree Algorithm

2014 ◽  
Vol 962-965 ◽  
pp. 2842-2847 ◽  
Author(s):  
Xiao Juan Chen ◽  
Zhi Gang Zhang ◽  
Yue Tong
Keyword(s):  
Decision Tree ◽  
Information Entropy ◽  
Information Gain ◽  
Classification Algorithm ◽  
Learning Ability ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Improved Algorithm

As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the number of the attributes values. The experiment result shows that the improved algorithm can get more reasonable and more effective rules.

An Improved ID3 Algorithm

2013 ◽  
Vol 444-445 ◽  
pp. 723-727 ◽  
Author(s):  
Ju Fang Li ◽  
Jin Hui Lei ◽  
Xiao Xia Zhao ◽  
Chang Chang Zhang ◽  
Xue Xue Han
Keyword(s):  
Decision Tree ◽  
Predictive Accuracy ◽  
Experimental Results ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Improved Algorithm

ID3 algorithm is the earliest and most influential of decision tree algorithm. This paper discusses the basic idea and implementation methods of the ID3 algorithm. A new algorithm based on attribute similarity for multivalued bias of ID3 algorithm was proposed, and the experimental results proved that the improved algorithm has strong predictive accuracy and better understandability.

An Improved SPRINT Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1685-1690 ◽  
Author(s):  
Zhi Kang Luo ◽  
Huai Ying Sun ◽  
De Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Learning Communities ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Gain Ratio ◽  
Information Gain Ratio ◽  
Improved Algorithm

This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.

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.

Classifier Ensemble Algorithm for Data Stream with Attribute Uncertainty

2016 ◽  
Vol 13 (10) ◽  
pp. 7519-7525 ◽  
Author(s):  
Zhang Xing ◽  
Wang MeiLi ◽  
Zhang Yang ◽  
Ning Jifeng
Keyword(s):  
Decision Tree ◽  
Data Stream ◽  
High Speed ◽  
Information Gain ◽  
Uncertain Data ◽  
Classifier Ensemble ◽  
Ensemble Classifiers ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Ensemble Algorithm

To build a classifier for uncertain data stream, an Ensemble of Uncertain Decision Tree Algorithm (EDTU) is proposed. Firstly, the decision tree algorithm for uncertain data (DTU) was improved by changing the calculation method of its information gain and improving the efficiency of the algorithm so that it can process the high-speed flow of data streams; then, based on this basic classifier, dynamic classifier ensemble algorithm was used, and the classifiers presenting effective classification were selected to constitute ensemble classifiers. Experimental results on SEA and Forest Covertype Datasets demonstrate that the proposed EDTU algorithm is efficient in classifying data stream with uncertain attribute, and the performance is stable under the different parameters.

scholarly journals Decision Tree Algorithm for Mining "If Then Else" Rule in Single Slope Basin Solar Still plant

2020 ◽  
Vol 9 (3) ◽  
pp. 405-410
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Information Gain ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Rule Mining ◽  
Rule Based ◽  
Tree Algorithm ◽  
C4.5 Decision Tree ◽  
Learning Concept

Soft computing dedicatedly works for decision making. In this domain a number of techniques are used for prediction, classification, categorization, optimization, and information extraction. Among rule mining is one of the essential methodologies. “IF Then Else” can work as rules, to classify, or predict an event in real world. Basically, that is rule based learning concept, additionally it is frequently used in various data mining applications during decision making and machine learning. There are some supervised learning approaches are available which can be used for rule mining. In this context decision tree is a helpful algorithm. The algorithm works on data splitting strategy using entropy and information gain. The data information is mapped in a tree structure for developing “IF Then Else” rules. In this work an application of rule based learning is presented for recycling of water in a distillation unit. By using the designed experimental still plant different attributes are collected with the observed distillated yield and instantaneous efficiency. This observed data is learned with the C4.5 decision tree algorithm and also predict the distillated yield and instantaneous efficiency. Finally to classify and predict the required parameters “IF Then Else” rules are prepared. The experimental results demonstrate, the proposed C4.5 algorithm provides higher accuracy as compared to similar state of art techniques. The proposed technique offers up to 5-9% improved outcome in terms of accuracy.

Application of Decision Tree Algorithm in Lumber Hierarchies

2012 ◽  
Vol 466-467 ◽  
pp. 308-313
Author(s):  
Dan Guo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Inductive Reasoning ◽  
Decision Tree Algorithm ◽  
Discrete Function ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Noise Data ◽  
Construction Model

The decision tree algorithm is a kind of approximate discrete function value method with high precision, construction model of classification of noise data is simple and has good robustness etc, it is currently the most widely used in one of the inductive reasoning algorithms in data mining, extensive attention by researchers. This paper selects the decision tree ID3 algorithm to realize the standardization of lumber level division, to ensure the accuracy of the lumber division, while improving the partition of speed.

scholarly journals Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhu Gu ◽  
Chaohu He
Keyword(s):  
Decision Tree ◽  
Mobile Computing ◽  
Classification Accuracy ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Data Set ◽  
Tree Algorithm ◽  
Id3 Algorithm ◽  
Fitness Industry

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.

Research on Decision Tree Algorithm Based on Information Entropy

2011 ◽  
Vol 267 ◽  
pp. 732-737 ◽  
Author(s):  
Ming Du ◽  
Shu Mei Wang ◽  
Gu Gong
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Information Entropy ◽  
Learning Method ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Knowledge Based

Decision tree is an important learning method in machine learning and data mining ,this paper discusses the method of choosing the best attribute based on information entropy .It analyzes the process and the characters of classification and the discovery knowledge based on decision tree about the application of decision tree on data mining .Through an instance ,the paper shows the procedure of selecting the decision attribute in detail ,finally it pointes out the developing trends of decision tree.

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.

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