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 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.

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.

scholarly journals High-Speed FIR Filter Design using Decision Tree Algorithm with FPGA Debugging

2020 ◽  
Vol 9 (3) ◽  
pp. 764-770
Keyword(s):  
Decision Tree ◽  
High Speed ◽  
Filter Design ◽  
Fir Filter ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Constant Multiplication ◽  
Field Programmable ◽  
Fir Filter Design ◽  
Complex Multiplier

In recent years, the filter is one of the key elements in signal processing applications to remove unwanted information. However, traditional FIR filters have been consumed more resources due to complex multiplier design. Mostly the complexity of the FIR filter is dominated by multiplier design. The conventional multipliers can be realized by Single Constant Multiplication (SCM) and Multiple Constant Multiplication (MCM) algorithms using shift and add/subtract operations. In this paper, a hybrid state decision tree algorithm is introduced to reduce hardware utilization (area) and increase speed in filter tap cells of FIR. The proposed scheme generates a decision tree to perform shift & addition and accumulation based on the combined SCM/MCM approach. The proposed FIR filter was implemented in Xilinx Field Programmable Gate Array (FPGA) platform by using Verilog language. The experimental results of the DTG-FIR filter were averagely reduced the 48.259% of LUTs, 51.567 % of flip flops and 44.497 % of slices at 183.122 MHz of operating frequency on the Virtex-5 than existing VP-FIR.

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.

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.

scholarly journals IMPLEMENTATION OF DECISION TREE AND K-NN CLASSIFICATION OF INTEREST IN CONTINUING STUDENT SCHOOL

2020 ◽  
Vol 17 (1) ◽  
pp. 51-56
Author(s):  
Daniati Uki Eka Saputri ◽  
Fitra Septia Nugraha ◽  
Taopik Hidayat ◽  
Abdul Latif ◽  
Ade Suryadi ◽  
...  
Keyword(s):  
High School Students ◽  
Decision Tree ◽  
Human Resources ◽  
Information Gain ◽  
Compulsory Education ◽  
Student Interest ◽  
Decision Tree Algorithm ◽  
School Students ◽  
Tree Algorithm

Education is important to prepare quality Human Resources (HR) because quality human resources is an important factor for the nation and state development. Therefore, it is expected that every citizen has the right to get high educational opportunities from the 12-year compulsory education level. This study aims to implement the Decision Tree and K-NN algorithm in the classification of student interest in continuing school.  This study proposes combining the Decision Tree and K-NN algorithm methods to improve accuracy with the Gain Ratio, Information Gain and Gini Index approaches for the measurement process. The test results show that the use of the Decision Tree algorithm produces an accuracy value of 97.30% while using the K-NN algorithm produces an accuracy of 89.60%. While the proposed method by combining the Decision Tree and K-NN algorithms produces an accuracy value of 98.07%. The results of evaluation measurements using the Area Under Curve (AUC) on the Decision Tree algorithm are 0.992 and the AUC on K-NN is 0.958 and on the combination of the Decision Tree and K-NN algorithms of 0.979. These results indicate that the proposed algorithm is very significant towards increasing accuracy in the classification of the interests of high school students continuing school

scholarly journals A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan

2018 ◽  
Vol 16 (1) ◽  
pp. 92 ◽  
Author(s):  
Ming-Shu Chen ◽  
Shih-Hsin Chen
Keyword(s):  
Metabolic Syndrome ◽  
Decision Tree ◽  
Older People ◽  
Information Gain ◽  
Data Driven ◽  
Decision Tree Algorithm ◽  
Middle Aged ◽  
Tree Structures ◽  
Tree Algorithm ◽  
The Metabolic Syndrome

According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.

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