Fuzzy Decision Tree Research Articles

Collaborative Search Engine for Enhancing Personalized User Search Based on Domain Knowledge.

In the fast moving world, users cross over large amount of data for their daily life. Due to the misinterpretation of the context, user cannot retrieve the proper context or failure to retrieve the information. The main aim of this paper is to design and implement a personalized search engine which works based on the domain of the user with the specific constraints suggested by the user. In this paper, the proposed system, build a search engine with web content which get information from the document corpus for the domain through the cloud databases. Web search engine re-ranks the generic results based on a ranking of a context linked with the domain. In this system, collaborative search service helps to improve the relevancy of the search results and to reduce the overtime on bad links and hence caters to customized needs with collaborative feedback using fuzzy decision tree based on fuzzy rules.

Fuzzy trees and forests—Review

AbstractData classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show the way they deal with data classification and regression problems. This paper is divided into four parts. In the first part we present the issue of fuzzy clustering, which is one of the most important aspects of fuzzy trees which base on clusters. Some methods of splitting objects into clusters using fuzzy logic are described there. The second part describes different fuzzy decision trees. The way these trees can deal with classification and regression problems is presented. In the third part the issue of forests—ensemble classifiers which consist of fuzzy trees—is described. The last part treats about the way of performing weighted decision making in fuzzy forests.This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Classification Technologies > Prediction Technologies > Machine Learning

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.

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

Classification of rubberized coir fibres using deep learning-based neural fuzzy decision tree approach

Indian coir industry is one among the small-scale cottage industries which contributes a great amount to the economy of India. India and Sri Lanka are the top two international producers, and they account for 90% of world coir manufacturing. The modern digital technologies can monitor the physical, chemical and microbiological properties in agriculture sector and can analyse the data in a faster manner. This paper highlights the large opportunities of deep learning in agriculture as an intelligent method for classifying and predicting the quality of the raw materials and thereby correlating into suitable products in the agricultural sector. Here we gathered the dataset from Central Coir Research Institute, Alappuzha, tested the chemical properties and applied several decision tree algorithms on the dataset and compared for analysis. This paper focuses specifically on a hybrid method with the usage of the back-propagation in deep learning and fuzzy logic decision tree. Experimental results reveal that the proposed method shows an accuracy of 98.75%. The comparison results with various decision tree-based classification algorithms, such as C4.5, ID3, CART, Naive Bayes, fuzzy, MLP, verify the effectiveness of the proposed method.

A parallel tree node splitting criterion for fuzzy decision trees

SummaryFuzzy decision trees are one of the most important extensions of decision trees for symbolic knowledge acquisition by fuzzy representation. Many fuzzy decision trees employ fuzzy information gain as a measure to construct the tree node splitting criteria. These criteria play a critical role in the construction of decision trees. However, many of the criteria can only work well on small‐scale or medium‐scale data sets, and cannot directly deal with large‐scale data sets on the account of some limiting factors such as memory capacity, execution time, and data complexity. Parallel computing is one way to overcome these problems; in particular, MapReduce is one mainstream solution of parallel computing. In this paper, we design a parallel tree node splitting criterion (MR‐NSC) based on fuzzy information gain via MapReduce, which is completed equivalent to the traditional unparallel splitting rule. The experimental studies verify the equivalency between the proposed MR‐NSC algorithm and the traditional unparallel way through 22 UCI benchmark data sets. Furthermore, the feasibility and parallelism are also studied on two large‐scale data sets.

Adaptive fuzzy neural production network with MIMO-structure for the evaluation of technology efficiency

The paper presents an example of modeling the algorithm operation. The paper analyses the modified Wang and Mendel MIMO-architecture of the adaptive fuzzy neural production network with a logical conclusion. It is distinguished by the automatic generation of a set of fuzzy rules based on a fuzzy decision tree and a hybrid algorithm for parameter adaptation by the neural network (centers, widths of membership functions, and conclusions) starting from the leaf nodes to the root nodes of the tree.

Wireless health monitoring with fuzzy decision tree for the community patients of chronic hypertension

Health is an important factor needed by humans. There are many facilities for indicators of human health learning, including through the internet. Health monitoring that can be seen anywhere, in this case, a clinic or hospital with internet technology is one way to monitor the patient’s condition and is presented in the form of real-time data. One device that can be online is digital tensimeter, especially for patients in the hypertension community. Health indicator data such as systole, diastole, and heart rate values, are obtained easily so that they can be used to analyze the patient’s condition. The method as used is to use the hardware device ESP 8266 from an offline tensimeter device so that the data on the real condition of the patient can be read anywhere. In addition, this device saves the number of computer network nodes to connect to the internet. After the data is read, the data is processed using Fuzzy decision tree to analyze which patients need immediate care. The result is that from 150 measured data, the value of decision tree accuracy with Fuzzy process is obtained at 95%

Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images

Manual detection and characterization of liver cancer using computed tomography (CT) scan images is a challenging task. In this paper, we have presented an automatic approach that integrates the adaptive thresholding and spatial fuzzy clustering approach for detection of cancer region in CT scan images of liver. The algorithm was tested in a series of 123 real-time images collected from the different subjects at Institute of Medical Science and SUM Hospital, India. Initially the liver was separated from other parts of the body with adaptive thresholding and then the cancer affected lesions from liver was segmented with spatial fuzzy clustering. The informative features were extracted from segmented cancerous region and were classified into two types of liver cancers i.e., hepatocellular carcinoma (HCC) and metastatic carcinoma (MET) using multilayer perceptron (MLP) and C4.5 decision tree classifiers. The performance of the classifiers was evaluated using 10-fold cross validation process in terms of sensitivity, specificity, accuracy and dice similarity coefficient. The method was effectively detected the lesion with accuracy of 89.15% in MLP classifier and of 95.02% in C4.5 classifier. This results proves that the spatial fuzzy c-means (SFCM) based segmentation with C4.5 decision tree classifier is an effective approach for automatic recognition of the liver cancer.

A Safety Computer System Based on Multi-Sensor Data Processing.

The safety computer in the train control system is designed to be the double two-vote-two architecture. If safety-critical multi-input data are inconsistent, this may cause non-strict multi-sensor data problems in the output. These kinds of problems may directly affect the decision making of the safety computer and even pose a serious threat to the safe operation of the train. In this paper, non-strict multi-sensor data problems that exist in traditional safety computers are analyzed. The input data are classified based on data features and safety computer features. Then, the input data that cause non-strict multi-sensor data problems are modeled. Fuzzy theory is used in the safety computer to process multi-sensor data and to avoid the non-strict multi-sensor problems. The fuzzy processing model is added into the onboard double two-vote-two architecture safety computer platform. The fuzzy processing model can be divided into two parts: improved fuzzy decision tree and improved fuzzy weighted fusion. Finally, the model is verified based on two kinds of data. Verification results indicate that the fuzzy processing model can effectively reduce the non-strict identical problems and improve the system efficiency on the premise of ensuring the data reliability.

Application of fuzzy decision tree in EOR screening assessment

Abstract Ranking of best possible Enhanced Oil Recovery (EOR) technics for implementing on a target field is one of the most important questions that should be answered by reservoir engineers. EOR screening can be considered as a tool for recommending the most appropriate EOR methods. Although for each candidate reservoir, the applicability of EOR processes must be investigated specifically, EOR screening can be used as an indicator before economic evaluations or reservoir descriptions are done and executive decisions are made. Implementing an EOR project for predictions that pass this screening is the next step. In this study, the fuzzy decision tree method (with the ability to rank and classify EOR methods simultaneously) is introduced for EOR screening. Basic features for this study are permeability, viscosity, depth, temperature, saturation, and API. Using a fuzzy decision tree enables us to design an expert system which generates EOR rules automatically. This is one of the noticeable features of this study which reduces the importance of a human expert role while designing the system and making it as expert as possible. Here, the fuzzy decision tree method is implemented on a dataset consisting of 548 observations related to 10 different EOR techniques. Predictions made by this method which are ranked from the most applicable EOR method to the least one include the EOR method mentioned in the dataset for every observation in both training and test set. Moreover, using the procedure introduced here for training the trees enables the expert system to be adaptive whenever the dataset is updated.

A Fuzzy-Based Approach for Identifying Candidate Business Processes for Socialization

Social business processes support enterprises tap into the opportunities of Web 2.0. To ensure an efficient support, this paper presents an approach for identifying an enterprise’s business processes that could be converted into social. Indeed, not all processes are eligible for this conversion. The approach is built upon the concept of enterprise architecture that sheds the light on the necessary contextual elements for guiding the socialization of business processes. Due to the fuzzy nature of socialization, two multi-criteria decision making techniques, fuzzy logic and fuzzy-decision tree, enhance this guidance. A set of experiments for evaluating the proposed approach are presented in the paper, as well.

Optimizing Partition Granularity, Membership Function Parameters, and Rule Bases of Fuzzy Classifiers for Big Data by a Multi-objective Evolutionary Approach

Classical data mining algorithms are considered inadequate to manage the volume, variety, velocity, and veracity aspects of big data. The advent of a number of open-source cluster-computing frameworks has opened new interesting perspectives for handling the volume and velocity features. In this context, thanks to their capability of coping with vague and imprecise information, distributed fuzzy models appear to be particularly suitable for handling the variety and veracity features of big data. Moreover, the interpretability of fuzzy models may assume a particular relevance in the context of big data mining. In this work, we propose a novel approach for generating, out of big data, a set of fuzzy rule–based classifiers characterized by different optimal trade-offs between accuracy and interpretability. We extend a state-of-the-art distributed multi-objective evolutionary learning scheme, implemented under the Apache Spark environment. In particular, we exploit a recently proposed distributed fuzzy decision tree learning approach for generating an initial rule base that serves as input to the evolutionary process. Furthermore, we integrate the evolutionary learning scheme with an ad hoc strategy for the granularity learning of the fuzzy partitions, along with the optimization of both the rule base and the fuzzy set parameters. Experimental investigations show that the proposed approach is able to generate fuzzy rule–based classifiers that are significantly less complex than the ones generated by the original multi-objective evolutionary learning scheme, while keeping the same accuracy levels.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.

Background: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients.Method: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR).Results: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria.Discussion: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS.Conclusions: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

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.

A general model for fuzzy decision tree and fuzzy random forest

AbstractThe problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function.

Fuzzy classification boundaries against adversarial network attacks

Adversarial machine learning copes with the development of methods to prevent machine learning algorithms from being misled by malicious users. This field is especially relevant for applications where machine learning lies in the core of security systems. In the field of network security, adversarial samples are actually novel network attacks or old attacks with tuned properties. This paper proposes to blur classification boundaries in order to enhance machine learning robustness and improve the detection of adversarial samples that exploit learning weaknesses. We test this concept by an experimental setup with network traffic in which linear decision trees are wrapped by a one-class-membership scoring algorithm. We benchmark our proposal with plain linear decision trees and fuzzy decision trees. Results show that evasive attacks (i.e., false negatives) tend to be ranked with low class-membership levels, meaning that they are located in zones close to classification thresholds. In addition, classification performances improve when membership scores are added as new features. Using fuzzy class boundaries is highly consistent with the interpretation of many network traffic features used for malware detection; moreover, it prevents network attackers from exploiting classification boundaries as attack objectives.

A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services

In a typical cloud environment, the task of identification and selection of required services solely depend upon the service specification furnished by the cloud user. The experienced users can specify their service requirements in precise terms by using numerical representations. On the other end, the task of service specification is always a challenging one for an inexperienced cloud user who seems to be new to the environment. In such a kind of requirement, vagueness arises in the preliminary phase of cloud service life cycle results service mismatches and affects both the cloud providers and user significantly. Hence, identifying and offering suitable services in responses to the imprecise service requirement have been emerged as an important research issue. In this paper, we have proposed a fuzzy logic-based intelligent cloud broker that clears the imprecise state of the inexperienced cloud user while furnishing the infrastructure service requirements. The proposed broker acts as an intermediate layer between the provider and consumer that finds out the appropriate services through the fuzzification and de-fuzzification process. In addition, the broker performs service aggregation through Sugeno integral and makes decision about the right services by implementing the fuzzy decision tree. The effectiveness of the broker has been validated by using MATLAB and R Studio.

Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees

With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems.

Tolerance rough fuzzy decision tree

Fuzzy decision tree (FDT) is an extension of decision tree. Fuzzy classification rules can be extracted by FDT from fuzzy decision tables with fuzzy conditional attributes and fuzzy decision attributes. However, it is very time consuming for fuzzifying conditional attributes, and fuzzification of conditional attributes will inevitably lead to information loss. In order to deal with this problem, based on tolerance rough fuzzy set, this paper proposed an algorithm named TRFDT (Tolerance Rough Fuzzy Decision Tree) and theoretically proved that the proposed algorithm is convergent with a very large probability. TRFDT can directly handle fuzzy decision tables with continuous-valued conditional attributes and fuzzy decision attributes. Accordingly, TRFDT has fast learning speed and good generalization ability, which have been experimentally proved by comparing TRFDT with two state-of-the-art approaches fuzzy ID3 and FDT-YS.