Fuzzy Decision Tree Research Articles

A three-way classification with fuzzy decision trees

This study is concerned with the design of a three-way classification mechanism realized through combing fuzzy decision trees and expressing uncertainty associated with classification results. The fuzzy decision tree used in this study is constructed through the generalization of the commonly used decision trees. The notion of three-way decision model first proposed for the interpretation of rules generated in rough set approximation has been widely used in many fields. This study proposes an efficient way to flag data with high level of uncertainty with the classification realized by fuzzy decision trees, which is the capability that commonly used fuzzy decision trees do not have. The data identified in this way are left to users’ judgments or more advanced classification techniques. The developed mechanism is formed as a two-stage construct where a fuzzy decision tree is built by generalizing the Boolean classification boundaries of a pre-constructed decision tree using fuzzy sets and then determining the level of uncertainty to identify instances to be rejected due to a lack of sufficient confidence in their belongingness. The rejected instances that are difficult to process are classified as non-commitment cases and left to some further analyses. The rejection quality of the developed three-way classifier is quantified in terms of the classification accuracy and rejection coefficient. We also elaborate on striking a sound tradeoff between these two performance indicators. Experimental studies demonstrate that the developed mechanism could effectively improve the classification accuracy at the cost of a small proportion of the rejected instances and achieve better performance in comparison with other three-way decision models when generating a three-way decision output.

Online Intrusion Detection for Internet of Things Systems With Full Bayesian Possibilistic Clustering and Ensembled Fuzzy Classifiers

The pervasive deployment of the Internet of Things (IoT) has significantly facilitated manufacturing and living. The diversity and continual updates of IoT systems make their security a crucial challenge, among which the detection of malicious network traffic turns out to be the most common yet destructive threat. Despite the efforts on feature engineering and classification backend designing, established intrusion detection systems sometimes lack robustness and are inflexible against the shift of the traffic distribution. To deal with these disadvantages, we design a fuzzy system for the online defense of IoT. Our framework incorporates a full Bayesian possibilistic clustering module for feature processing and an ensemble module motivated by reinforcement learning and adaptive boosting that dynamically fits the streaming data. The proposed clustering module overcomes the issue of determining the number of clusters and can dynamically identify new patterns. The classifier backend combines a collection of fuzzy decision trees that provide readable decision boundaries. The ensembled classifiers can accommodate the drift of data distribution to optimize the long-time performance. Our proposal is tested on settings including one dataset collected from real IoT systems and is compared to numerous competitors. Experimental results verified the advantage of our system regarding accuracy and stability.

Multi-classifier and meta-heuristic based cache pollution attacks and interest flooding attacks detection and mitigation model for named data networking

ABSTRACT This study’s main goal is to provide a novel assault detection model with two phases. The features such node, distribution, pattern, frequency, and run time are extracted in the first phase. The Deep Convolutional Neural Network (CNN) is then used to detect cache pollution attacks (also known as content poisoning attacks), which include interest flooding for existing data, interest flooding for non-existing data, hijacking incoming interest packets, and signing data with the incorrect key. Similar to the first phase, the second phase involves extracting the aforementioned attributes and feeding them into a fuzzy decision tree (FDT) in order to identify an interest flooding attack. For accurate attack detection, this paper aims to optimise both DCNN and FDT classifiers. This work provided a fresh Decision Oriented Rider Optimisation Algorithm (DO-ROA), an enhancement of the traditional ROA, to address the optimisation problem. With regard to specific Type I and Type II performance measures, the suggested DO-ROA algorithm’s performance is compared to that of other traditional models, demonstrating the superiority of the presented work.

A new method for analysis of Multi-State systems based on Multi-valued decision diagram under epistemic uncertainty

The development of the system's model is an important step in reliability analysis. The system's model in reliability analysis similar to other knowledge areas is used as an explanatory and research tool. In reliability analysis, this model is mathematical and allows us to determine quantitative characteristics to evaluate the behavior of the original system. Typically such a mathematical model approximates the behavior of the initial system and has some uncertainty. However, this uncertainty can increase significantly if the initial data for building the model is uncertain and incompletely specified. The uncertainty of the mathematical model also causes incorrect estimates of the system behavior and its reliability. Therefore, it is important to develop methods that allow us to take into account the uncertain nature of the initial data, first of all, take into account epistemic uncertainty in initial data. A new method for the development of a mathematical model of a Multi-State System (MSS) in the form of a Multi-Valued Decision Diagram based on incompletely specified and uncertain data is proposed. In other words, the proposed method takes into account the epistemic uncertainty of the initial data. The specific of this method is the use of Data Mining based classification procedures, in particular, the Fuzzy Decision Trees.

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.

IoT enabled healthcare system for predicting the diseases using feature optimization, decision tree, neural network, and fuzzy temporal rules

SummaryDiabetes, cancer, and heart diseases are identified to be fatal for human beings due to the lack of awareness, erratic food habits, lack of physical work and so forth. Many disease prediction systems are introduced by various researchers. However, early diagnosis of the diseases and monitoring systems are not working effectively and it increases the death rate. Recently, Internet of Things (IoT) is incorporated to collect the instant data from patient and the system makes effective decisions. Many IoT enabled healthcare systems are available for predicting the specific diseases but such effective healthcare system for measuring the severity of diabetes, cancer and heart diseases is scarce. For this purpose, a new neuro‐fuzzy model is used in the proposed IoT enabled healthcare system for disease prediction, which combines fuzzy rules with temporal features. First, it categorizes the available features of the patient records. Second, a new temporal decision tree is introduced for learning the patient records carefully and predicting the record by using the available data. Third, the fuzzy temporal decision tree calculates the relationship between the available features and also selects them as contributed features. In addition, some of the features are identified as not useful for making a final decision, and these are to be removed. Fourth, the fuzzy rules are to be generated by using the Mamdani fuzzy membership function. Finally, a new framework is proposed which is the combination of neuro‐fuzzy temporal networks, fuzzy temporal decision trees, and fuzzy temporal rules. The proposed model is evaluated by conducting experiments and proved as better than the existing systems in terms of prediction accuracy, error rate, and efficiency.

A fuzzy content-based group recommender system with dynamic selection of the aggregation functions

Recommender systems are currently software tools that are focused on providing users with the best choices in an overloaded search space of possible options. Hence, group recommender systems have recently become an important trend in recommendation, because they aim at recommending a special type of items so-called social items, that tend to be consumed in groups such as TV programs, travel packages, etc. Among the different types of algorithms applied for group recommender systems, this paper is focused on content-based group recommender systems, as a novel group recommendation paradigm that exploits item features in the recommendation generation process. Specifically, our goal is to introduce a new content-based group recommendation approach, based on the recommendation aggregation paradigm whose main novelty is the development of a dynamic selection process of the aggregation scheme. Such an approach is centered on the identification of group's characteristics that are matching with the most appropriate function to use in the individual recommendation aggregation step. To perform such a matching, it is proposed a fuzzy decision tree induction process. The experimental evaluation shows that this scheme improves the recommendation performance of previous content-based group recommendation approaches, as well as it serves a starting point for further research based on this dynamic selection paradigm.

Continuous Dynamic Update of Fuzzy Random Forests

Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.

Interpretable fuzzy clustering using unsupervised fuzzy decision trees

In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems.

Scalable decision tree based on fuzzy partitioning and an incremental approach

<span>Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then Fuzzy Information Gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.</span>

Information granulation-based fuzzy partition in decision tree induction

In this study, an interval information granulation-based fuzzy partition (InterIG-FP) method is established to define fuzzy items in the framework of fuzzy decision tree induction. The proposed method first employs the principle of justifiable granularity to build information granules in terms of different classes on different condition attributes, where the number of information granules is equal to the number of classes. Then, the average values of the representative samples determined by each information granule are utilized to define the membership functions of fuzzy items for each condition attribute. Finally, an interval information granulation-based fuzzy decision tree (InterIG-FDT) is constructed based on the predefined fuzzy items in a top-down recursive way. The experiments illustrate the effectiveness, comparability and immunity capability of the proposed method. The effectiveness is verified by some comparative analysis with several traditional no-fuzzy decision trees. Furthermore, under the same constructing framework, some fuzzy decision trees with different fuzzy partition methods are studied to show the comparability on classification accuracies and the immunity capability on noise data, respectively.

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.

Evaluation of machine learning techniques to select marine oil spill response methods under small-sized dataset conditions

Oil spill incidents can significantly impact marine ecosystems in Arctic/subarctic areas. Low biodegradation rate, harsh environments, remoteness, and lack of sufficient response infrastructure make those cold waters more susceptible to the impacts of oil spills. A major challenge in Arctic/subarctic areas is to timely select suitable oil spill response methods (OSRMs), concerning the process complexity and insufficient data for decision analysis. In this study, we used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection. Using a small hypothetical oil spill dataset, the modelling performance was thoroughly compared to find techniques working well under data constraints. The regression-based machine learning models were also compared with integrated and optimized fuzzy decision trees models (OFDTs) previously developed by the authors. OFDTs and GPR outperformed other techniques considering prediction power (> 30 % accuracy enhancement). Also, the use of the Bayesian regularization algorithm enhanced the performance of ANNs by reducing their sensitivity to the size of the training dataset (e.g., 29 % accuracy enhancement compared to an unregularized ANN).

Explainable CNN With Fuzzy Tree Regularization for Respiratory Sound Analysis

Auscultation is an important tool for diagnosing respiratory-related diseases. Unfortunately, the quality of auscultation is limited by the professional level of the doctor and the environment of the auscultation. Some studies have focused on automated auscultation techniques. However, existing approaches suffer from two challenges: 1) the models cannot learn from data distributed among multiple hospitals and 2) the predictions of the models are difficult to interpret for physicians. To address this issue, this article proposes a novel explainable respiratory sound analysis framework with fuzzy decision tree regularization. This framework develops an ensemble knowledge distillation technique to learn distributed data and achieves good performance in terms of model efficiency and accuracy. Fuzzy decision trees are used to explain the predictions of the model and produce decision rules that can be well accepted by physicians. The effectiveness of this framework is thoroughly validated on the Respiratory Sound database and compared with other existing approaches.

METHOD OF COMPUTER SYSTEM STATE IDENTIFICATION BASED ON BOOSTING ENSEMBLE WITH SPECIAL PREPROCESSING PROCEDURE

The subject of the research is methods of identifying the state of the Computer System. The object of research is the process of identifying the state of a computer system for information protection. The aim of the research is to develop the method for identifying the state of a computer system for information protection. This article is devoted to the development of method (boosting ensemble) to increase the accuracy of detecting anomalies in computer systems. Methods used: artificial intelligence methods, machine learning, decision tree methods, ensemble methods. The results were obtained: a method of computer system identification based on boosting ensemble with special preprocessing procedure is developed. The effectiveness of using machine learning technology to identify the state of a computer system has been studied. Experimental researches have confirmed the effectiveness of the proposed method, which makes it possible to recommend it for practical use in order to improve the accuracy of identifying the state of the computer system. Conclusions. According to the results of the research, ensemble classifier of computer system state identification based on boosting was proposed. It was found that the use of the proposed classifier makes it possible to reduce the variance to 10%. In addition, due to the optimization of the initial data, the efficiency of identifying the state of the computer was increased. Prospects for further research may be to develop an ensemble of fuzzy decision trees based on the proposed method, optimizing their software implementation.

EEG Signal Classification Based On Fuzzy Classifiers

Electroencephalogram (EEG) signal classification is used in many applications. Typically, this classification is implemented based on methods which consist of two steps. These steps are known as the step of signal preprocessing and the step of the classification. The signal preprocessing step transforms initial signal into classification attributes. According to several studies, this transformation can result in the loss of some useful information and, consequently, the formed classification attributes are uncertain. This information loss can be taken into account if the classification attributes are fuzzy and the fuzzy classifiers are used at the step of classification itself. The transformation of initial EEG signal into fuzzy attributes needs one more procedure at the step of signal preprocessing. This procedure is fuzzification. An approach based on fuzzy classifiers for EEG signal classification is considered in this article. The approach is evaluated based on two classifiers: fuzzy decision tree and fuzzy random Forest. The classification accuracy is 99.5% for fuzzy decision tree and 99.3% for fuzzy random forest. The comparison with similar studies based on non-fuzzy classifiers indicates that fuzzy classifiers are effective tool for EEG signal classification and have best classification accuracy.

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.

An Extractive Text Summarization based on Candidate Summary Sentences using Fuzzy-Decision Tree

This study aims to predict candidate summary sentences in extractive summary using the Fuzzy-Decision Tree method. The fuzzy method is quite superior and the most widely used in extractive summaries, because Fuzzy has advantages in calculations that are not cryptic, so it is able to calculate uncertain possibilities. However, in its implementation, the fuzzy rule generation process is often carried out randomly or based on expert understanding so that it does not represent the distribution of the data. Therefore, in this study, a Decision Tree (DT) technique was added to generate fuzzy rules. From the fuzzy final result, important sentences are obtained that are candidates for summary sentences. The performance of our proposed method was tested on the 2002 DUC dataset in the ROUGE-1 evaluation. The results showed that our method outperformed other methods (baseline and sentence ranking) with an average precision of 0.882498, Recall 0.820443 and F Measure 0.882498 with CI for F1 0.821-0.879 at the 95% confidence level.

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.