Formalizing natural-language spatial relations descriptions with fuzzy decision tree algorithm

2006 ◽  
Author(s):  
Jun Xu ◽  
Changqing Yao
Keyword(s):  
Decision Tree ◽  
Natural Language ◽  
Spatial Relations ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Algorithm

scholarly journals P1835PREDICTION OF CHRONIC OF KIDNEY FAILURE BASED ON ARTIFICIAL INTELLIGENCE SYSTEM USING THE FUZZY DECISION TREE ALGORITHM

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Rifaldy Fajar ◽  
Prihantini Jupri
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kidney Failure ◽  
Laboratory Tests ◽  
Chronic Kidney Failure ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Algorithm

Abstract Background and Aims Chronic kidney disease is one type of disease that can cause death. Until now, chronic kidney failure has no antidote, so this disease cannot be cured but can be slowed down or stopped its development. Early diagnosis of this disease will help to prevent these fatal consequences. To diagnose this disease, several laboratory tests are needed in which the results of these tests will be calculated and concluded the results by a doctor or medical practitioner. The development of science and technology, especially in the field of computers will help the work of doctors to analyze the results of laboratory tests become easier and faster. In this study, a prediction attempt is made using the Fuzzy Decision Tree classification algorithm, which is expected to obtain high accuracy results. Method This study uses the Chronic Kidney Disease (CKD) dataset taken from the UCI Machine Learning Repository. Data was collected from the hospital for approximately two months. This dataset covers a total of 400 samples with numerical attributes totaling 11 columns and nominal totaling 14 columns. Data samples were provided as many as 400 rows with 250 samples being the ckd group (positive for chronic kidney failure) and 150 samples for the notckd group (chronic kidney failure). But after going through the preprocessing stage, data that can be used amounted to 158 rows with 43 samples are the ckd group (positive chronic kidney failure) and 115 samples of the notckd group (negative chronic kidney failure). Results The trial was conducted using several predetermined thresholds and the most optimal accuracy was 98.3%, which showed a fairly high degree of accuracy. Conclusion Thus, it can be concluded that the Fuzzy Decision Tree algorithm can be said to be able to predict chronic kidney failure with a very good results.

scholarly journals Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Li
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Tree Algorithm ◽  
English Teaching ◽  
K Nearest Neighbor Algorithm

As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

scholarly journals Smart Smoking Area based on Fuzzy Decision Tree Algorithm

2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Iswanto ◽  
Kunnu Purwanto ◽  
Weni Hastuti ◽  
Anis Prabowo ◽  
Muhamad Yusvin
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Algorithm

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.

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