Fuzzy Decision Tree Algorithm Research Articles

Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

Computer-Aided Medical Analysis of Smart University Student's Psychological Fitness Data using Fuzzy Clustering and Decision Tree Algorithms

Computer-Aided Medical Analysis of Smart University Student's Psychological Fitness Data using Fuzzy Clustering and Decision Tree Algorithms

Retracted: Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

Retracted: Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

Retracted: English Teaching Quality Evaluation Based on Analytic Hierarchy Process and Fuzzy Decision Tree Algorithm.

[This retracts the article DOI: 10.1155/2022/5398085.].

Retracted: Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

Retracted: Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

Lithology identification using principal component analysis and particle swarm optimization fuzzy decision tree

Lithology identification using geophysical log information is vital for log interpretation and reservoir evaluation. As a result of the highly similar features for log curves that characterize complex lithology, there is significant information redundancy regarding the process of lithology identification. In addition, as a result of the highly nonlinear characteristics of log curves, the mapping relationship with lithology has certain ambiguities and uncertainties, which affect the lithology prediction results. Combining principal component analysis (PCA) and the fuzzy decision tree (FDT) model, we propose a new intelligent lithology identification method that is capable of effectively solving these problems well. However, because of the inaccuracy for empirically set parameters, an adaptive fuzzy decision tree algorithm based on particle swarm optimization (PSO-FDT) was proposed after analyzing the main features of the fuzzy decision tree and using an improved particle swarm optimization (PSO) algorithm to determine the relevant parameters. Compared with the FDT algorithm which determines parameter values empirically, the performance of the PSO-FDT has been significantly improved. Finally, the proposed PSO-FDT model was verified using test data. Experiments confirm that the proposed model is more effective than other lithology identification models. The identification accuracy for all lithologies was equal to or greater than that of the other methods. In addition, the overall accuracy was improved by at least 9.71%.

Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm

With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorithm can achieve high accuracy in the four databases, and the classification accuracy of the A-D database is 75.9%, 62.9%, 76.6%, and 95.1%, respectively. Except for database C, the classification accuracy of the minimum classification uncertainty algorithm is lower than the other two clear decision tree algorithms. Compared with the minimum classification uncertainty algorithm, the fuzzy ID3 algorithm has obvious advantages in classification rules. The classification rules of A-D database are 18, 12, 16, and 10, respectively. When the authenticity threshold is about 0.8, the fuzzy ID3 algorithm has the highest classification accuracy. The proposed MOOCS model for college physical education based on the fuzzy decision tree algorithm has strong practicability and high accuracy. This paper studies the MOOCS model of college physical education and introduces the fuzzy decision tree algorithm to evaluate the MOOCS model of college physical education. It solves the problem that traditional sports cannot satisfy the needs of “Internet plus education” at all. Compared with the traditional sports model, it has better applicability and higher accuracy.

English Teaching Quality Evaluation Based on Analytic Hierarchy Process and Fuzzy Decision Tree Algorithm.

China's economy has become more globalized in the twenty-first century. English has become the primary means of communication and trade between people and other countries as the economy has rapidly developed. As a result, colleges and universities have increased the English proficiency and teaching quality criteria for college students. The success of college instructors' English instruction is judged using teaching quality as a primary metric. The procedure of assessing the quality of English instruction is complicated. Using appropriate assessment indexes and procedures, a scientific and sensible evaluation system for English education should be developed. This study uses the AHP and fuzzy decision tree algorithms to investigate the quality of teaching (preferably English) and gives teaching evaluation data to university administrators, which is useful to enhance the quality of teaching (preferably English) in colleges. First, a hierarchical analysis-based evaluation index system (EIS) for English teaching is established, followed by a detailed description of the decision tree algorithm's calculation process and the application of the fuzzy decision tree algorithm to the evaluation of teaching (preferably English) quality. Finally, the practical application effect of the algorithm is tested. The results show that the accuracy of this algorithm is higher than other algorithms, and it is helpful to improve the efficiency of evaluating English teaching quality.

MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm.

In recent years, the development of science and technology in China has greatly affected people's ways of entertainment. In the traditional industrial model, new industries and Internet industries represented by the Internet have emerged, and the Internet video business is an emerging business that has been gradually emerging in the Internet industry in recent years. Moreover, this new teaching method has been gradually noticed in simple education, such as MOOC, I want to self-study network, and Smart Tree, and other online learning websites have sprung up. At present, the epidemic environment makes people pay more attention to this convenient and wide range of online video education. Therefore, we need to evaluate this kind of online video teaching model from the effectiveness of this kind of method and the quality of user experience. This paper takes this as the starting point and chooses the earliest online video platform, MOOC, as the model to establish a set of perfect user experience quality evaluation methods suitable for domestic online video education mode. Considering the data source, the accuracy of the results, and other factors, we chose the industry-leading platform MOOC network as an example. Through the exploration of the MOOC teaching mode in basic education, a member experience evaluation model is established based on fuzzy decision tree algorithm. The experimental results show that the model has high accuracy and high reliability.

Construction and Application of Enterprise Internal Audit Data Analysis Model Based on Decision Tree Algorithm

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K-means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision-making system based on Fuzzy ID3 and Min-Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min-Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk-oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value.

A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree

Lithology identification methods based on conventional logging data are essential in reservoir geological evaluation. Due to the highly non-linear relationship between lithology and various logging parameters, conventional methods cannot meet the requirements. In recent years, machine learning methods such as Neural Networks and decision tree have been applied to the field of lithology identification and achieved good effects. However, there is no obvious difference in logging parameters for various types of lithology, and at the same time, there is a large amount of information redundancy between each logging curve. Therefore, its uncertainty and fuzziness are high, which interferes with the result of lithology identification. Combining fuzzy theory, decision tree and K-means++ algorithm, this paper proposes a novel hybrid technique of lithology identification which can better overcome the ambiguity and uncertainty of logging data. In the actual data test, we select six logging parameters: density (RHOB), neutron porosity (NPHI), natural gamma (GR), longitudinal wave velocity (VP), shallow formation resistivity (LLS), and deep formation resistivity (LLD). Then K-means++ clustering algorithm was used for clustering analysis on logging data. Finally, the triangular membership function is selected to fuzz the logging data according to the obtained clustering center points, and a fuzzy decision tree lithology identification model is constructed. The prediction accuracy of the model reached 93.92%. The fuzzy decision tree algorithm was also compared with five machine learning algorithms, including decision tree, extremely randomized trees (ET), Adaboost, random forest (RF) and gradient boosting decision tree (GBDT). The results show that the modeling results of fuzzy decision tree algorithm outperform other algorithms. In summary, the fuzzy decision tree model developed in the study is a practical and effective model for complex lithology identification, providing a new idea for lithology identification. • A fuzzy decision tree model using logging data for lithology identification is proposed. • The k-means++ algorithm optimizes the selection of initial clustering centers. • Logging data is fuzzily processed by the triangular membership function. • Pre-pruning the fuzzy decision tree by controlling the values of significance level α and truth degree threshold β. • The accuracy of the model can reach 93.92%, which is better than other algorithms.

Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control

Increasing the growth of big data, particularly in healthcare-Internet of Things (IoT) and biomedical classes, tends to help patients by identifying the disease early through methods for the analysis of medical data. Hence, nanotechnology-based IOT biosensors play a significant role in the medical field. Problem. However, the consistency continues to decrease where missing data occurs in such medical data from nanotechnology-based IOT biosensors. Furthermore, each region has its own special features, which further lowers the accuracy of prediction. The proposed model initially reconstructs lost or partial data in order to address the challenge of handling the medical data structures with incomplete data. Methods. An adaptive architecture is proposed to enhance the computing capabilities to predict the disease automatically. The medical databases are managed by unpredictable environments. This optimized paradigm for diagnosis produces the fuzzy, genetically categorized decision tree algorithm. This work uses a normalized classifier namely fuzzy-based decision tree (FDT) algorithm for classifying the data collected via nanotechnology-based IOT biosensors, and this helps in the identification of nondeterministic instances from unstructured datasets relating to the medical diagnosis. The FDT algorithm is further enhanced by using genetic algorithms for effective classification of instances. Finally, the proposed system uses two larger datasets to verify the predictive precision. In order to describe a fuzzy decision tree algorithm based upon the fitness function value, a modified decision classification rule is used. The structure and unstructured databases are configured for processing. Results and Conclusions. This evaluation of test patterns helps to track the efficiency of FDT with optimized rules during the training and testing stages. The proposed method is validated against nanotechnology-based IOT biosensors data in terms of accuracy, sensitivity, specificity, andF-measure. The results of the simulation show that the proposed method achieves a higher rate of accuracy than the other methods. Other metrics relating to the model with and without feature selection show an improved sensitivity, specificity, andF-measure rate than the existing methods.

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

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.

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

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.

P1835PREDICTION OF CHRONIC OF KIDNEY FAILURE BASED ON ARTIFICIAL INTELLIGENCE SYSTEM USING THE FUZZY DECISION 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.

Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks

Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.

IMPLEMENTASI FUZZY DECISION TREE UNTUK PREDIKSI PENYAKIT LIVER PADA DATASET ILPD (INDIAN LIVER PATIENT DATASET)

Hati manusia adalah salah satu organ utama dalam tubuh dan penyakit liver dapat menyebabkan banyak masalah dalam kehidupan manusia. Hati mengubah zat beracun menjadi nutrisi dan kemudian tubuh menggunakannya untuk mengendalikan tingkat hormon dalam tubuh. Prediksi penyakit liver yang cepat dan akurat memungkinkan perawatan dini dan efektif. Perkembangan dari teknologi komputer membuat pekerjaan diagnosis atau pengambilan keputusan menjadi lebih mudah. Di dalam machine learning memungkinkan sebuah komputer mampu untuk memutuskan atau memberikan saran yang tepat. Uji coba telah dilakukan menggunakan algoritme fuzzy decision tree dengan menggunakan beberapa nilai threshold yaitu fuzziness control threshold (𝜃𝑟) dan leaf decision threshold (𝜃𝑛) yang telah di tentukan dengan metode uji coba 10-fold cross validation dan diperoleh hasil akurasi sebesar 78.95%. Dikarenakan akurasi tersebut di peroleh di beberapa nilai threshold yang berbeda, maka untuk mengetahui kinerja akurasi yang baik digunakanlah metode receiver operating characteristics (ROC) sehingga hasil ROC menunjukkan bahwa akurasi yang paling optimal 78.95% berada saat 𝜃𝑟=75%,77%,80%,82%,85%, 87%,90%,92%,95%,98% dan 𝜃𝑛=6%.Kata Kunci: Penyakit Liver, Fuzzy Decision Tree, K-Fold Cross Validation dan Receiver Operating Characteristics

An autonomous resource provisioning framework for massively multiplayer online games in cloud environment

Massively Multiplayer Online Games (MMOGs) applications are a class of computationally intensive client-server multi-tier applications with real-time quality of service (QoS) requirements. In these online games, the players all over the world can interact with each other through a virtual environment. To guarantee the QoS requirements to a highly variable number of concurrent players, the game operators statically over-provision a large infrastructure capable of sustaining the game peak load, even though a large portion of the resources is unused most of the time and subsequently leads to incur high resource provisioning costs on MMOG providers. To address this problem, we introduce an autonomous resource-provisioning framework for MMOGs in a Cloud environment. Firstly, a load prediction service anticipates the future game-entity distribution from historical trace data using an adaptive neuro-fuzzy inference system (ANFIS) prediction model. Then, we proposed a fuzzy decision tree algorithm to estimate the proper number of resources to be allocated to each tier in the MMOG application using the predicted workload and user SLA. The experimental results under real and synthetic workloads indicate that the proposed solution outperforms in terms of accuracy and performance metrics compared with the other approaches.

Smart Smoking Area based on Fuzzy Decision Tree Algorithm

Cigarette smoke is very dangerous for both active and passive smokers who smoke inside a room because nicotine from cigarette smoke can stick on the wall or in the furniture and produce carcinogenic substances when reacting with air. The carcinogen chemicals in cigarettes are more dangerous when cigarette smoke is trapped in a limited space. An exhaust fan is usually used in a special room for smokers that serves to remove cigarette smoke without exchanging air in it. A smart smoking room tool specifically for smokers was made to answer the problem. The room used an ‘in and out’ exhaust fan ventilator. This fan ventilator rotated based on the quantity of carbon monoxide (CO) gas in the room detected by using a smoke sensor. Arduino Uno based on Fuzzy Decision Tree algorithm was used to control of the input voltage level in the fan ventilator. The result showed that by using the tool, the cigarette smoke in the room can be controlled effectively.

New classification technique: fuzzy oblique decision tree

Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.