Fuzzy Decision Tree Research Articles

A fuzzy intelligent system for land consolidation – a case study in Shunde, China

Abstract. Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision-making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. A group of indexes need to be selected for land consolidation. In this article, a fuzzy measure was adopted to accomplish the selection of specific features. A fuzzy integral based on a fuzzy measure is a type of fusion tool. The optimal solution with the fewest non-zero elements was obtained for the fuzzy measure by solving a fuzzy integral. This algorithm provides a quick and optimal way to identify the land-index system when preparing to conduct land consolidation. This new research was applied to Shunde's "Three Old" consolidation project which provides the data. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove to be consistent, and the new model is more automatic and intelligent. The results of this estimation system are significant for informing decision-making in land consolidation.

MFlexDT: multi flexible fuzzy decision tree for data stream classification

In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

Control and optimization of human perception on virtual garment products by learning from experimental data

This paper proposes an original study for controlling, in an Internet and virtual reality-based collaborative design platform, human perception on 3D virtual garments by adjusting fabric parameters, constituting the inputs to the garment CAD software. This study will permit the designer to determine the most relevant product through a number of interactions with the consumer. For this purpose, two sensory experiments have been realized on a small number of fabric samples. In the first experiment, we propose an active learning-based experimental design in order to find the most appropriate values of the fabric technical parameters permitting to minimize the overall perceptual difference between real and virtual fabrics in static and dynamic scenarios. The second sensory experiment aims to extract normalized tactile and visual sensory descriptors characterizing human perception on the concerned fabric samples. In the collaborative design process, these normalized descriptors will be used for communications between the designer and the consumer on perceptual quality of virtual products. By learning from the experimental data on identified inputs (fabric parameters of the CAD software) and outputs (sensory descriptors), we model the relationship between fabric technical parameters and human perception on finished virtual products. The method of fuzzy ID3 decision tree has successfully been applied in this modeling procedure. This model, combined with the corresponding garment CAD software and the learning data acquired from the two sensory experiments, constitutes the main components of the learning data-driven collaborative design platform. Using this platform, we have realized a number of garments meeting consumer’s personalized perceptual requirements.

A Study on the Approximation of Clustered Data to Parameterized Family of Fuzzy Membership Functions for the Induction of Fuzzy Decision Trees

Abstract This paper investigates the Triangular, Trapezoidal and Gaussian approximation methods for the purpose of induction of Fuzzy Decision Trees (FDT). The generation of FDT is done using a Fuzzy ID3 induction algorithm. In this work three fuzzy partitioning techniques which form the basis for our investigation are given attention, namely Fuzzy C Means clustering (FCM), Grid partitioning and Subtractive clustering (Subclust). Our contribution lies in studying the effect of various approximations on the generation of FDT giving specific attention to the classification accuracy of FDT. In this study we show that the accuracy levels of FDT generated using FCM clustered raw data, bypassing the approximation step, is acceptable and this method has several advantages too. Several computational experiments are conducted and non parametric statistical tests are performed to find if any significant differences exist between the method of bypassing the approximation step and the other methods which include approximation. Ten FDTs are developed and used in this study. These FDT’s differ in their fuzzy partitioning techniques and the approximation methods used.

Analog circuits fault diagnosis using multi‐valued Fisher's fuzzy decision tree (MFFDT)

SummaryFault diagnosis of analog circuits is more challenging compared with digital circuits as a result of the parametric deviation and the difficulty in signal discretization. There still lacks effective approaches to realize reliable fault detection and isolation for a comprehensive diagnosis. A new fault diagnosis technique called multi‐valued Fisher's fuzzy decision tree (MFFDT) is proposed in this paper to solve the problem. This technique uses the decision tree as the diagnosis model and incorporates the Fisher's linear discriminant principles. The fuzzification mechanism is devised to discretize the input monitoring data. The proposed MFFDT method is composed of two aspects: decision tree training and real fault diagnosis processes. The former uses the benchmark data to train a decision tree, while the latter sends the monitoring data into the decision tree to generate diagnosis results. The proposed method is validated using simulated data and the real‐time data for an active filter circuit and an audio amplifying circuit. The comparative analysis is also presented to evaluate diagnosis performances. Copyright © 2015 John Wiley & Sons, Ltd.

VR-BFDT: A variance reduction based binary fuzzy decision tree induction method for protein function prediction

In protein function prediction (PFP) problem, the goal is to predict function of numerous well-sequenced known proteins whose function is not still known precisely. PFP is one of the special and complex problems in machine learning domain in which a protein (regarded as instance) may have more than one function simultaneously. Furthermore, the functions (regarded as classes) are dependent and also are organized in a hierarchical structure in the form of a tree or directed acyclic graph. One of the common learning methods proposed for solving this problem is decision trees in which, by partitioning data into sharp boundaries sets, small changes in the attribute values of a new instance may cause incorrect change in predicted label of the instance and finally misclassification. In this paper, a Variance Reduction based Binary Fuzzy Decision Tree (VR-BFDT) algorithm is proposed to predict functions of the proteins. This algorithm just fuzzifies the decision boundaries instead of converting the numeric attributes into fuzzy linguistic terms. It has the ability of assigning multiple functions to each protein simultaneously and preserves the hierarchy consistency between functional classes. It uses the label variance reduction as splitting criterion to select the best “attribute-value” at each node of the decision tree. The experimental results show that the overall performance of the proposed algorithm is promising.

Classification of Breast Cancer using Gini Index based Fuzzy Supervised Learning in Quest Decision Tree Algorithm

Decision tree is a dominating method of pattern classification. These trees amongst the machine learning techniques have an aptitude to handle and construe logical rules of classification. The general Decision trees face the problem on deciding boundaries in classification. A fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm aimed at constructing a fuzzy decision boundary instead of puny decision boundaries. The intended work deals with a fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm by calculating the Gini index at the points where the class information changes. This algorithm helps in predicting benign and malignant breast cancer cases more effectively. The breast cancer mammographic mass dataset (BI-RADS) was taken from UCI Machine Learning Repository, center for machine learning and intelligent systems. A 3-fold cross validation of train data and test data on BI-RADS dataset was used and the proposed Fuzzy SLIQ Decision Tree algorithm was applied on it. The proposed method‟s performance was superior to earlier techniques. The examined results in partitioning the benign and malignant cases using Fuzzy SLIQ Decision Tree is more promising with a classification accuracy of 81.4% which is more prominent then many of the existing classifier techniques which used BI-RADS dataset in classification of breast cancer cases.

Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis

A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.

Intelligent Fashion Recommender System: Fuzzy Logic in Personalized Garment Design

This paper proposes a new intelligent fashion recommender system to select the most relevant garment design scheme for a specific consumer in order to deliver new personalized garment products. This system integrates emotional fashion themes and human perception on personalized body shapes and professional designers’ knowledge. The corresponding perceptual data are systematically collected from professional using sensory evaluation techniques. The perceptual data of consumers and designers are formalized mathematically using fuzzy sets and fuzzy relations. The complex relation between human body measurements and basic sensory descriptors, provided by designers, is modeled using fuzzy decision trees. The fuzzy decision trees constitute an empirical model based on learning data measured and evaluated on a set of representative samples. The complex relation between basic sensory descriptors and fashion themes, given by consumers, is modeled using fuzzy cognitive maps. The combination of the two models can provide more complete information to the fashion recommender system, making it possible to evaluate if a specific body shape is relevant to a desired emotional fashion theme and which garment design scheme can improve the image of the body shape. The proposed system has been validated in a customized design and mass market selection through the evaluations of target consumers and fashion experts using a method frequently used in marketing study.

Fuzzy decision tree to classify complex fractionated atrial electrograms.

Catheter ablation has emerged as an effective treatment strategy for atrial fibrillation (AF) in recent years. During AF, complex fractionated atrial electrograms (CFAE) can be recorded and are known to be a potential target for ablation. Automatic algorithms have been developed to simplify CFAE detection, but they are often based on a single descriptor or a set of descriptors in combination with sharp decision classifiers. However, these methods do not reflect the progressive transition between CFAE classes. The aim of this study was to develop an automatic classification algorithm, which combines the information of a complete set of descriptors and allows for progressive and transparent decisions. We designed a method to automatically analyze CFAE based on a set of descriptors representing various aspects, such as shape, amplitude and temporal characteristics. A fuzzy decision tree (FDT) was trained and evaluated on 429 predefined electrograms. CFAE were classified into four subgroups with a correct rate of 81±3%. Electrograms with continuous activity were detected with a correct rate of 100%. In addition, a percentage of certainty is given for each electrogram to enable a comprehensive and transparent decision. The proposed FDT is able to classify CFAE with respect to their progressive transition and may allow objective and reproducible CFAE interpretation for clinical use.

Inducting Fuzzy Decision Tree Based on Discrete Attributes through Uncertainty Reduction

In this paper, we give a fuzzy decision tree (simply FDT) induction algorithm, named FDTAmbig, to handle the classification with discrete attributes through the uncertainty reduction. In FDTAmbig, the uncertainty is measured with classification ambiguity. FDTAmbig selects the attribute which will cause the further reduction of uncertainty as the expanded attribute for each decision node. The experimental result shows that FDTAmbig has the better generalization capability in comparison with the FDT induced with classification entropy (FDTEntr).

A fuzzy method for discovering cost-effective actions from data

Data mining techniques are often confined to the delivery of frequent patterns and stop short of suggesting how to act on these patterns for business decision-making. They require human experts to post-process the discovered patterns manually. Therefore a significant need exists for techniques and tools with the ability to assist users in analyzing a large number of patterns to find usable knowledge. Action mining is one of these techniques which intelligently and automatically suggests some changes in the state of an object with the aim of gaining some profit in the corresponding domain. Up to now little research has been done in this field; in all cases continuous-valued data is handled by discretizing the associated attributes in advance or during the learning process. One inherent disadvantage in these methods is that using this sharp behavior can result in missing the optimal action. To overcome this problem this paper presents a method based on fuzzy set theory. In this paper, we concentrate on the fuzzy set based approach for the enhancement of Yang's method and present an algorithm that suggests actions which will decrease the degree to which a certain object belongs to an undesired status and increase the degree to which it belongs to a desired one. Our algorithm takes into account the fuzzy cost of actions, and further, it attempts to maximize the fuzzy net profit. The contribution of the work is in taking the output from fuzzy decision trees, and producing novel, actionable knowledge through automatic fuzzy post-processing. The performance of the proposed algorithm is compared with Yang's method using several real-life datasets taken from the UCI Machine Learning Repository. Experimental results show that the proposed algorithm outperforms Yang's method not only in finding more actions but also in finding actions with more fuzzy net profit.

Revisiting the theory of social capital through the financing of microentrepreneurs in Cameroon using fuzzy decision tree and QFD

The purpose of this study was to determine the networks of financing generated by social capital for informal microentrepreneurs in Cameroon and the extent to which they are important. The data collected and used were secondary and were from an employment survey done between 2005 and 2010 by the National Institute of Statistics. Percentages and ordinary least square regression were used to carry out descriptive and explanatory analyses respectively. The results showed that the social capital enables informal microentrepreneurs to get access to a diversified network of financiers made up of formal, informal financial and non-financial institutions. It was clear that the higher the social capital, the more microentrepreneurs use informal sources to get access to finances. It was equally revealed that social capital significantly explained the financing of microentrepreneurs within the area and scope of study. Based on these findings, we recommend that the government should put in place a regulatory framework facilitating the development of specific institutions in charge of financing the informal microentrepreneurs in Cameroon.

Data-Mining-Based Coronary Heart Disease Risk Prediction Model Using Fuzzy Logic and Decision Tree.

ObjectivesThe importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans.MethodsA model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model.ResultsThe rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction.ConclusionsThe accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.

A MULTI-CRITERIA DECISION-MAKING APPROACH THAT COMBINES FUZZY TOPSIS AND DEA METHODOLOGIES

Employee selection is a multi-criteria decision-making (MCDM) problem for selecting suitable applicants from a ready pool. The selection aims to make use of their knowledge, relevant skills, and other characteristics to perform a specific job. The aim of this study is to develop a systematic approach for selecting the best candidates among the air traffic controllers (ATCs) for aviation in Saudi Arabia. Three integrated methods were employed for decision-making in this study. First, a fuzzy decision tree was applied to determine the criteria weights, then the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the attributes. In the last step, the Data Envelopment Analysis (DEA) was used to transform the qualitative variables into quantitative equivalences. A survey was conducted by national and international decision- makers to elicit the necessary information on the criteria and sub-criteria of the air traffic control system. The decision problem was formulated by employing five criteria and ten applicants. The relationship between the fuzzy TOPSIS and fuzzy-weighted average was very positive for decision-making. The outcomes of the fuzzy TOPSIS and DEA encouraged the development of a decision support system for the selection of ATCs.

Interval-valued fuzzy decision trees with optimal neighbourhood perimeter

This research proposes a new model for constructing decision trees using interval-valued fuzzy membership values. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values, however, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the look-ahead based fuzzy decision tree induction method to construct decision trees. We also investigate the significance of different neighbourhood values and define a new parameter insensitive to specific data sets using fuzzy sets. Some examples are provided to demonstrate the effectiveness of the approach.

Fuzzy rule based decision trees

This paper presents a new architecture of a fuzzy decision tree based on fuzzy rules – fuzzy rule based decision tree (FRDT) and provides a learning algorithm. In contrast with “traditional” axis-parallel decision trees in which only a single feature (variable) is taken into account at each node, the node of the proposed decision trees involves a fuzzy rule which involves multiple features. Fuzzy rules are employed to produce leaves of high purity. Using multiple features for a node helps us minimize the size of the trees. The growth of the FRDT is realized by expanding an additional node composed of a mixture of data coming from different classes, which is the only non-leaf node of each layer. This gives rise to a new geometric structure endowed with linguistic terms which are quite different from the “traditional” oblique decision trees endowed with hyperplanes as decision functions. A series of numeric studies are reported using data coming from UCI machine learning data sets. The comparison is carried out with regard to “traditional” decision trees such as C4.5, LADtree, BFTree, SimpleCart, and NBTree. The results of statistical tests have shown that the proposed FRDT exhibits the best performance in terms of both accuracy and the size of the produced trees.

Fuzzy Decision Tree

Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree. It proposes a fuzzy decision tree induction method in iris flower data set, obtaining the entropy from the distance between an average value and a particular value. It also presents an experiment result that shows the accuracy compared to former ID3.

Detection and Classification of Islanding and Nonislanding Events in Distributed Generation Based on Fuzzy Decision Tree

This paper presents a new approach for islanding detection of distributed generation systems (DGs), using the features obtained from a new time-frequency transform with the negative sequence voltage, negative sequence current and the 3-phase voltage and current signals as inputs. The well-known S-transform suffers from high computational complexity, for on-line applications and hence, a new time-frequency transform (frequency filtering S-transform or simply the FFST) similar to it but faster by almost 30 times is proposed here using the frequency sampling and band pass filtering only for neighborhood of significant frequency components, determined from FFT of the power disturbance signals at the DG terminals. Thus using only the important frequency components present in the signal, it results in a significant reduction of computational complexity of the discrete S-transform. A data mining approach using a certainty factor-based fuzzy decision tree is used to yield fuzzy rules with the extracted features from the FFST output to recognize disturbance events like islanding or non-islanding for a variety of operating conditions of both the DGs and the electric power system. The results achieved during testing using existing hybrid distribution systems show that the proposed method is very reliable, and fast even in the presence of large disturbances like faults and capacitor bank switching.

Study on Fault Diagnosis Method for Nuclear Power Plant Based on Fuzzy Rough Sets and Decision Tree

The technology of real-time fault diagnosis for nuclear power plants (NPP) has great significance to improve the safety and economy of reactor. Nuclear power plants are complex system, which collect and monitor the vast parameters. A parameter reduction method based on fuzzy rough sets was proposed. According to the characteristics the parameters were fuzzed, and they were reducted using the algorithm of forward greedy search. The decision tree was applied to learn from training samples which were the typical faults of nuclear power plant, i.e., loss of coolant accident (LOCA), feed water pipe rupture, steam generator tube rupture (SGTR), main steam pipe rupture, and diagnose by using the acquired knowledge. The result shows that this method can diagnose the faults of the NPP rapidly and accurately.