Fuzzy Decision Tree Research Articles

소프트 컴퓨팅 기법을 이용한 개인화된 손동작 인식 시스템

최근 하지가 불편한 노약자나 장애인이 집안의 다양한 가전기기를 손쉽게 제어하기 위한 비전 기반의 손동작 인식 기술이 발전해 왔다. 다수의 사용자가 하나의 손동작 인식 시스템을 사용할 경우 사용자마다 손동작 특성이 모두 다르기 때문에 특정 사용자의 인식률이 저하되는 문제가 발생한다. 또한 동일한 사용자라 하더라도 시간에 따라 손동작 특성이 변화할 수 있다. 사용자마다 다른 손동작 특성은 모델 학습 및 선택 기법을 사용해 효과적으로 다루어질 수 있다. 시간에 따라 변하는 사용자의 특성은 퍼지 개념을 이용해 효과적으로 다루어질 수 있다. 본 논문에서는 다변량 퍼지 의사 결정트리를 이용해 사용자 별 인식모델을 만드는 방법을 제시한다. 또한 새로운 사용자가 시스템을 사용할 경우 가장 적합한 모델을 선택해 인식에 사용하고 인식률을 측정한다. Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees

This article compares the decision‐tree algorithm (C5.0), neural decision‐tree algorithm (NDT) and fuzzy decision‐tree algorithm (FIDs) for addressing reservoir operations regarding water supply during normal periods. The conventional decision‐tree algorithm, such as ID3 and C5.0, executes rapidly and can easily be translated into if‐then‐else rules. However, the C5.0 algorithm cannot discover dependencies among attributes and cannot treat the non‐axis‐parallel class boundaries of data. The basic concepts of the two algorithms presented are: (1) NDT algorithm combines the neural network technologies and conventional decision‐tree algorithm capabilities, and (2) FIDs algorithm extends to apply fuzzy sets for all attributes with membership function grades and generates a fuzzy decision tree. In order to obtain higher classification rates in FIDs, the flexible trapezoid fuzzy sets are employed to define membership functions. Furthermore, an intelligent genetic algorithm is utilized to optimize the large number of variables in fuzzy decision‐tree design. The applicability of the presented algorithms is demonstrated through a case study of the Shihmen Reservoir system. A network flow optimization model for analyzing long‐term supply demand is employed to generate the input‐output patterns. Findings show superior performance of the FIDs model in contrast with C5.0, NDT and current reservoir operating rules.

A Fuzzy Decision Tree for Fault Classification

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

Soft Discretization in a Classification Model for Modeling Adaptive Route Choice with a Fuzzy ID3 Algorithm

This study introduces a way to overcome the sensitivity of decision trees used for route choice behavior studies by using fuzzy logic while preserving the advantages of decision trees and the C4.5 algorithm, namely, comprehensibility and ease of application. Soft discretization of continuous values in fuzzy decision trees can provide a more robust classification. Also, the use of fuzzy logic makes it possible to accommodate qualitative attributes describing route characteristics. Apart from these features, fuzzy decision tree learning algorithms are also capable of assigning numeric values on decisions about the degree of certainty of each recommendation emanating from the fuzzy reasoning. This feature makes it possible to solve the multiple suggestion problem whereby the classic decision tree may suggest that more than one route is optimal. To investigate improvements resulting from the application of fuzzy decision tree learning algorithms, software for an adaptive route choice model using a fuzzy decision tree learning algorithm, fuzzy ID3, was developed and simulation experiments with the model were carried out. The comparison of results with the nonfuzzy adaptive route choice model indicates better predictive accuracy and more effective applicability for the fuzzy model in practice.

Classification of Heterogeneous Fuzzy Data by Choquet Integral With Fuzzy-Valued Integrand

As a fuzzification of the Choquet integral, the defuzzified choquet integral with fuzzy-valued integrand (DCIFI) takes a fuzzy-valued integrand and gives a crisp-valued integration result. In this paper, the DCIFI acts as a projection to project high-dimensional heterogeneous fuzzy data to one-dimensional crisp data to handle the classification problems involving different data forms, such as crisp data, interval values, fuzzy numbers, and linguistic variables, simultaneously. The nonadditivity of the signed fuzzy measure applied in the DCIFI can represent the interaction among the measurements of features towards the discrimination of classes. Values of the signed fuzzy measure in the DCIFI are considered to be unknown parameters which should be learned before the classifier is used to classify new data. We have implemented a genetic algorithm (GA)-based adaptive classifier-learning algorithm to optimally learn the signed fuzzy measure values and the classified boundaries simultaneously. The performance of our algorithm has been tested both on synthetic and real data. The experimental results are satisfactory and outperform those of existing methods, such as the fuzzy decision trees and the fuzzy-neuro networks.

FRCT: fuzzy-rough classification trees

Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules, average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.

A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system

A fuzzy reasoning and verification Petri nets (FRVPNs) model is established for an error detection and diagnosis mechanism applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree and a Petri nets technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program.

Brain tumor characterization using the soft computing technique of fuzzy cognitive maps

The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps (FCMs) to represent and model experts’ knowledge (experience, expertise, heuristic). The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm. The proposed method was validated for clinical material, comprising of 100 cases. FCM grading model achieved a diagnostic output of accuracy of 90.26% (37/41) and 93.22% (55/59) for brain tumors of low-grade and high-grade, respectively. The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.

A modular holistic approach to prosody modelling for Standard Yorùbá speech synthesis

This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yorùbá (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293–324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yorùbá. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse.

A Heuristic Approach to Learning Rules from Fuzzy Databases

As an alternative to approaches based on entropy and information gain, we describe a system that uses a measure called the impurity level. The learning algorithm based on this measure, which we call FARNI, first induces fuzzy decision trees by using an impurity-level extension for selecting the best branch. This is similar to the way C4.5 and ARNI induce selections for crisp databases. Once FARNI calculates the fuzzy decision tree, it returns compact fuzzy rule sets that apply a pruning process

Backward design and cross-functional design management

This study establishes a framework for backward design analyses and cross-functional performance management system to achieve product design for excellence. The authors define backward design as opposed to regular forward design. A cross-functional viewpoint emphasizing downstream knowledge and lessons learned is introduced for proactive product design management. The study proposes several approaches for backward design endeavors, including Extended Failure Mode and Effect Analysis (EFMEA), Attributes Function Deployment (AFD), Feedback Tracking and Analysis System (FeTAS), Fuzzy Decision Tree, etc. Based on the backward design concerns, a cross-functional design management system is then established to serve as an incentive system for designer to incorporate cross-functional concerns. The contributions include: (1) Proposing backward design mechanisms; (2) Establishing a cross-functional design management system which is conducive to design for excellence.

Fuzzy Decision Tree Based Inference Techniques for Network Forensic Analysis

Network forensics is an important extension to present security infrastructure, and is becoming the research focus of forensic investigators and network security researchers. However many challenges still exist in conducting network forensics: The sheer amount of data generated by the network; the comprehensibility of evidences extracted from collected data; the efficiency of evidence analysis methods, etc. Against above challenges, by taking the advantage of both the great learning capability and the comprehensibility of the analyzed results of decision tree technology and fuzzy logic, the researcher develops a fuzzy decision tree based network forensics system to aid an investigator in analyzing computer crime in network environments and automatically extract digital evidence. At the end of the paper, the experimental comparison results between our proposed method and other popular methods are presented. Experimental results show that the system can classify most kinds of events (91.16% correct classification rate on average), provide analyzed and comprehensible information for a forensic

Incorporating Uncertainty via Hierarchical Classification Using Fuzzy Decision Trees

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Object hierarchy is often ignored when collecting and classifying geographical objects. Object attributes are defined on the basis of uncertain parameters that may change in space and time. In this paper, we consider fuzzy decision trees for classification and a Bayesian hierarchical model for modeling and handling uncertainty. The study is illustrated with dynamic geographical objects from a coastal management application in the northern part of The Netherlands. Hierarchical modeling is applied to obtain posterior distributions for several boundary regions. The posterior distributions yield lower and upper limits of membership functions describing boundaries between object classes. In this way, a proper fuzzy decision tree for the coastal management application is built, which includes the inherent dynamic uncertainty. </para>

Two levels decision system for efficient planning and implementation of bioenergy production

Abstract When planning bioenergy production from biomass, planners should take into account each and every stakeholder along the biomass supply chains, e.g. biomass resources suppliers, transportation, conversion and electricity suppliers. Also, the planners have to consider social concerns, environmental and economical impacts related with establishing the biomass systems and the specific difficulties of each country. To overcome these problems in a sustainable manner, a robust decision support system is required. For that purpose, a two levels general Bioenergy Decision System (gBEDS) for bioenergy production planning and implementation was developed. The core part of the gBEDS is the information base, which includes the basic bioenergy information and the detailed decision information. Basic bioenergy information include, for instance, the geographical information system (GIS) database, the biomass materials’ database, the biomass logistic database and the biomass conversion database. The detailed decision information considers the parameters’ values database with their default values and the variables database, values obtained by simulation and optimization. It also includes a scenario database, which is used for demonstration to new users and also for case based reasoning by planners and executers. Based on the information base, the following modules are included to support decision making: the simulation module with graph interface based on the unit process (UP) definition and the genetic algorithms (GAs) methods for optimal decisions and the Matlab module for applying data mining methods (fuzzy C-means clustering and decision trees) to the biomass collection points, to define the location of storage and bioenergy conversion plants based on the simulation and optimization model developed of the whole life cycle of bioenergy generation. Furthermore, Matlab is used to set up a calculation model with crucial biomass planning parameters (e.g. costs, CO2 emissions), over the predefined biomass supply chains. The GIS based interface allows visualization of all bioenergy conversion plants and storage data to support the users’ decision based on quantifiable outputs. With the help of the graphical interface, the users can define easily the feasible biomass supply chains, take decisions and evaluate them according to their own interests, might they be environmental, economical, social or others. Therefore, the gBEDS supports biomass energy planners in both national and regional levels to assess the options (technically and economically) of biomass utilization and plan for bioenergy generation in a competent and sustainable way.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.

On poem recognition

Literature is one of the most significant forms of human culture. It represents a high level of intellectual activity. Humans write, read, and enjoy poems in different cultures. The purpose of this work is to initiate research in the field of poem recognition, not to understand them by electronic devices, but to provide humans with modern tools to search for and work with lyrics. This study is concerned to illuminate the challenges of poem recognition from different perspectives. In this paper, various measures of poetry are introduced and their effectiveness are investigated in different case studies using fuzzy logic, bayesian approach, and decision trees.

Intelligent data analysis with fuzzy decision trees

Intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.

A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis

In this paper, we present syllable-based duration modelling in the context of a prosody model for Standard Yorùbá (SY) text-to-speech (TTS) synthesis applications. Our prosody model is conceptualised around a modular holistic framework. This framework is implemented using the Relational Tree (R-Tree) techniques. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration, intonation, and intensity, using different techniques and their subsequent integration. We applied the Fuzzy Decision Tree (FDT) technique to model the duration dimension. In order to evaluate the effectiveness of FDT in duration modelling, we have also developed a Classification And Regression Tree (CART) based duration model using the same speech data. Each of these models was integrated into our R-Tree based prosody model. We performed both quantitative (i.e. Root Mean Square Error (RMSE) and Correlation (Corr)) and qualitative (i.e. intelligibility and naturalness) evaluations on the two duration models. The results show that CART models the training data more accurately than FDT. The FDT model, however, shows a better ability to extrapolate from the training data since it achieved a better accuracy for the test data set. Our qualitative evaluation results show that our FDT model produces synthesised speech that is perceived to be more natural than our CART model. In addition, we also observed that the expressiveness of FDT is much better than that of CART. That is because the representation in FDT is not restricted to a set of piece-wise or discrete constant approximation. We, therefore, conclude that the FDT approach is a practical approach for duration modelling in SY TTS applications.

A fuzzy decision tree to predict phosphorus export at the catchment scale

Qualitative understanding of the processes controlling phosphorus (P) export from agricultural land has been significantly improved in recent years. Problems remain in predicting P losses despite the requirement of tools providing accurate predictions by legislation such as the EU Water Framework Directive. Decision making, not only in the field of diffuse pollution, often relies on limited data. This study is aiming to predict annual P export from agricultural catchments using a very simple approach that concentrates on the functional behaviour of a catchment. Two simple fuzzy decision trees have been established to predict both total P filtered at 0.45 μm (TP <0.45 ) and particulate P (TP >0.45 ) export. The predictions are within range of the P export estimated from measured data using discharge–concentration rating curves. The fuzzy method is capable of identifying the catchments having high P export and reproduces the pattern of P export for wet and dry years, especially for TP <0.45 . The predicted fuzzy ranges for TP >0.45 export are wide. The available data indicate that single events have a high importance for TP >0.45 export. We assume that an event-based decision tree might be the appropriate approach to constrain the uncertainties. The proposed methodology is simple. For both trees, a classification is made based on only four input variables using fuzzy rules. The rules do not depend on the estimation of numerous parameters but can easily be adapted once new information becomes available. Therefore, the fuzzy system has a high potential to be used as a decision support tool for policy makers.

Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree

Seasonal factor is an important parameter for converting coverage counts to annual average daily traffic (AADT). There have been many studies on establishing seasonal factor (SF) categories, but there is a limited understanding of how to assign SF groups to short-term count sites. In the current practice, established factor groups are typically assigned based on function class, the physical proximity of short count sites to a permanent traffic count site, and other engineering judgment. There is no sound theoretical basis or rules to guide the seasonal factor assignment process, which results in inaccurate AADT estimates for coverage count sites. This paper describes a data-driven procedure for assigning a seasonal factor category to a given portable count site by taking into consideration the similarities between the characteristics of permanent count sites in the seasonal factor group and the portable count site. A fuzzy decision tree was constructed based on the known SF groupings of permanent count sites and their land use attributes. The decision tree was then applied to determine the SF category for a given portable count site in Broward, Miami-Dade, and Palm Beach counties in Florida.