Fuzzy Decision Tree Research Articles

Ant colony optimization based fuzzy binary decision tree for bimodal hand knuckle verification system

In the recent trends of touch-less biometric authentication systems, hand knuckles from dorsal part of the hand is gaining popularity as a potential candidate for verification/recognition in variety of security applications. However, most of the available knuckle verification systems offer fixed security achieved for desired level of accuracy which cannot meet the varying levels of security requirements. This paper presents a bimodal knuckle verification system which is designed to meet a wide range of applications varying from civilian to high security regions. We use ant colony optimization (ACO) to choose the optimal fusion parameters corresponding to each level of security. The developed verification system utilizes fuzzy binary decision tree (FBDT) which is aimed at decision making in two classes: genuine (accept) and imposter (reject) using matching scores computed from the knuckle database. The FBDT is implemented using fuzzy Gini index for the selection of the tree nodes. The experiments are carried out on four publicly available HongKong PolyU knuckle databases named as: left index, right index, left middle and right middle with four bimodal systems: left–right index, left–right middle, left index–middle and right index–middle. The experimental results from these four bimodal knuckle databases validate the contributions of the proposed work.

Fuzzy binary decision tree for biometric based personal authentication

The use of fuzzy decision trees is yet to be ascertained for the biometric based personal authentications. This paper therefore presents a fuzzy binary decision tree (FBDT) algorithm for decision making on two classes: genuine and imposter using matching scores computed from the biometric databases. The proposed FBDT makes use of two criteria: fuzzy Gini index and fuzzy entropy for the selection of the tree nodes. The fuzzy membership functions can be automatically computed from the training scores and these are employed in two modes: Same function mode, where only one membership function is used for both the classes and Different function mode, where separate functions are used for both the classes. The parameters computed at the learning stages are used for the classification of the claimed identity in any of the two classes. Over-fitting of feature data often results in false branches in the decision trees. So the pruning of the tree is required with the consequent increase in computational complexity. Most of the FBDTs in this work are found to have lesser size than DTs as ascertained from the experimental results. The proposed FBDT is tested on two publically available databases and it fares well over its crisp counterpart.

Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction

Sample selection is to select a number of representative samples from a large database such that a learning algorithm can have a reduced computational cost and an improved learning accuracy. This paper gives a new sample selection mechanism, i.e., the maximum ambiguity-based sample selection in fuzzy decision tree induction. Compared with the existing sample selection methods, this mechanism selects the samples based on the principle of maximal classification ambiguity. The major advantage of this mechanism is that the adjustment of the fuzzy decision tree is minimized when adding selected samples to the training set. This advantage is confirmed via the theoretical analysis of the leaf-nodes' frequency in the decision trees. The decision tree generated from the selected samples usually has a better performance than that from the original database. Furthermore, experimental results show that generalization ability of the tree based on our selection mechanism is far more superior to that based on random selection mechanism.

Adaptive Fuzzy Decision Tree with Dynamic Structure for Automatic Process Control System of Continuous Cast Billet Production

The article outlines the results of adaptive fuzzy decision tree development with dynamic structure for automatic process control system of continuous cast billet production. The authors described the structure, mathematical representation as well as adaptation algorithms and structure dynamics.

A HYBRID SOFTWARE COST ESTIMATION APPROACH UTILIZING DECISION TREES AND FUZZY LOGIC

Software cost estimation (SCE) is one of the critical activities in software project management. During the past decades various models have been proposed for SCE. However, developing accurate and useful models is limited in practice despite the considerable financial gain they could offer to software stakeholders. Traditional techniques, such as regression, by-analogy and machine learning, face the difficulty of handling the dynamic nature of the software process and the problematic nature of the public data available. This paper addresses the issue of SCE proposing an alternative approach that combines robust decision tree structures with fuzzy logic. Fuzzy decision trees are generated using the CHAID and CART algorithms in a systematic manner, while development effort is treated as the dependent variable against two subsets of factors: The first contains selected attributes from the ISBSG, COCOMO and DESHARNAIS datasets and the second contains a subset of the available factors that can be measured early in the development cycle. The association rules obtained from the trees are then merged and defuzzified through a Fuzzy Implication System (FIS). The fuzzy framework is utilized to perform effort estimations. Experimental results indicate that the proposed approach is promising as it yields quite accurate estimations in most dataset cases considered. Finally, our evaluation suggests that accurate estimations may be produced, even when using only a small set of factors that can be measured early in the development cycle, thus increasing the practical value of the proposed cost model.

Real Life Applications of Fuzzy Decision Tree

Fuzzy Decision Tree is becoming increasingly significant as it is applied to areas of different platforms in real life. This paper gives an overview of the applications of fuzzy decision tree in heterogeneous fields. It is being actively used in fields as varied as intrusion detection, flexible querying (modus ponens), analysis of cognitive process (Human Computer Interaction), for user authentication in biometrics, as parallel processing support, in stock-market, for information retrieval and data mining. The fuzzy logic approach allows the direct incorporation of expertise. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation evolves. The powerful combinatorial methods found in fuzzy logic have been used to prove fundamental results in other areas namely medical, educational, chemical and multimedia.

Investigating Effort Prediction of Software Projects on the ISBSG Dataset

Many cost estimation models have been proposed over the last three decades. In this study, we investigate fuzzy ID3 decision tree as a method for software effort estimation. Fuzzy ID software effort estimation model is designed by incorporating the principles of ID3 decision tree and the concepts of the fuzzy settheoretic; permitting the model to handle uncertain and imprecise data whenpresenting the software projects. MMRE (Mean Magnitude of Relative Error) and Pred(l) (Prediction at level l) are used, as measures of prediction accuracy, for this study. A series of experiments is reported using ISBSG software projects dataset. Fuzzy trees are grown using different fuzziness control thresholds. Results showed that optimizing the fuzzy ID3 parameters can improve greatl y the accuracy of the generated software cost estimate.

Erratum to: “Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules”

Erratum to: “Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules”

Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study

Condensate-to-gas ratio (CGR) plays a significant role in sales potential assessment of both gas and liquid, design of the required surface processing facilities, and reservoir characterization and modeling in gas-condensate reservoirs. Precise field and laboratory determination of the CGR is time and people intensive. Developing a rapid and inexpensive technique for accurate estimation of the CGR is inevitable. To tackle this issue, an intelligent model was proposed and the purpose of this paper is to illustrate how Fuzzy Decision Tree (FDT), which is an automatic method of generating fuzzy rules, can predict the condensate gas ratio, as a vital parameter in order to design the necessary wellhead production facilities in retrograde gas reservoir. As the FDT method is strongly based on applying widely and effectively the concept of ambiguity and furthermore, to do this project more accurately and less dependent on experts' knowledge, it was decided to gain from piecewise linear membership functions (MFs) whose parameters have automatically been dedicated through calculating a very special method of possibility density function (pdf). In conclusion, according to the calculated result, it is possible to exploit this method for CGR prediction field wide. The Fuzzy model was evaluated using the experimental data combined with some PVT data from the open literature. The model predictions were compared with the data from the actual field data and experimental runs. Results from this study indicate that the developed model can predict the CGR with good precision. The proposed model can serve as a reliable tool for quick and cheap but effective assessment of the CGR in absence of any adequate laboratory or field data. General Terms Fuzzy Logic, Artificial Intelligence

GA Guided Cluster Based Fuzzy Decision Tree for Reactive Ion Etching Modeling: A Data Mining Approach

There are various data mining techniques that are frequently used for the mining of vital patterns embedded within bulk data. These techniques include neural network, regression analysis, rough set theory, Bayesian network, decision trees, and so on. In this research, a novel data mining technique, genetically guided cluster based fuzzy decision tree (GCFDT), is introduced for the mining task. In order to test the efficacy of GCFDT, it is employed for building the predictive process models of reactive ion etching (RIE) with the aid of optical emission spectroscopy (OES) signals. This model endeavors to predict the wafer surface conditions for the new incoming set of process parameters. OES is an efficient tool for monitoring plasma emission intensity. In contrast with the C-fuzzy decision tree where granules are devolved through fuzzy clustering here, granulation is practised through genetically guided fuzzy clustering. The growth of the tree is governed by expanding the node having highest diversity. The results obtained by employing CGFDT in RIE process modeling reveal that it dominates both the traditional C-fuzzy decision trees and C4.5 decision trees in terms of both the accuracy and compactness.

Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering

This paper presents a new approach for processing various non-stationary power quality waveforms through a Fast S-Transform with modified Gaussian window to generate time–frequency contours for extracting relevant feature vectors for automatic disturbance pattern classification. The extracted features are then clustered using Bacterial Foraging Optimization Algorithm (BFOA) based Fuzzy decision tree to give improved classification accuracy in comparison to the Fuzzy decision tree alone. To circumvent the problem of premature convergence of BFOA and to improve classification accuracy further, a hybridization of BFOA (Bacterial Foraging Optimization Algorithm) with another very popular optimization technique of current interest called Differential Evolution (DE) is presented in this paper. For robustness the mutation loop of the DE algorithm has been made variable in a stochastic fashion. This hybrid algorithm (Chemotactic Differential Evolution Algorithm (CDEA)) is shown to overcome the problems of slow and premature convergence of BFOA and provide significant improvement in power signal pattern classification.

SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images

A novel fuzzy decision tree is proposed in this paper (the FDT-support vector machine (SVM) classifier), where the node discriminations are implemented via binary SVMs. The tree structure is determined via a class grouping algorithm, which forms the groups of classes to be separated at each internal node, based on the degree of fuzzy confusion between the classes. In addition, effective feature selection is incorporated within the tree building process, selecting suitable feature subsets required for the node discriminations individually. FDT-SVM exhibits a number of attractive merits such as enhanced classification accuracy, interpretable hierarchy, and low model complexity. Furthermore, it provides hierarchical image segmentation and has reasonably low computational and data storage demands. Our approach is tested on two different tasks: natural forest classification using a QuickBird multispectral image and urban classification using hyperspectral data. Exhaustive experimental investigation demonstrates that FDT-SVM is favorably compared with six existing methods, including traditional multiclass SVMs and SVM-based binary hierarchical trees. Comparative analysis is carried out in terms of testing rates, architecture complexity, and computational times required for the operative phase.

Using decision trees for investment project evaluating

Traditionally, decision trees are used for graphical representation of the set of possible alternatives which decision maker has on disposal. Recently, decision trees are not used only as a graphical wireframe in decision making. Moreover, together with fuzzy sets, they are used for alternative evaluating and evaluating of the project as a whole providing an optimal long-term investment strategy for decision maker. Fuzzy decision trees, a generalization of decision trees in fuzzy environment, are used for extracting fuzzy rules from training data and leads to approximate reasoning in economics.

ECA Rule Learning for Resource Management in Grid Computing: Fuzzy Approach

 Abstract—Grid computing is important environment for resource sharing and distributed system integration. One discussed field in Grid computing is the resource management. In this paper, Grid-JQA architecture is used that includes active database. We focus resource management with regarding to active rule learning. Rule learning is very significant for efficient rules in active database system. Fuzzy Decision tree can use into rule update. Results shows this approach can be effectively applied for rule learning in such environments because there is attribute with continuous values and splitting at node must be dynamic.

Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules and decision tree rules

Abstract The development of medical domain applications has been one of the most active research areas recently. One example of a medical domain application is a detection system for heart disease based on computer-aided diagnosis methods, where the data is obtained from some other sources and is evaluated by computer based applications. Up to now, computers have usually been used to build knowledge based clinical decision support systems which used the knowledge from medical experts, and transferring this knowledge into computer algorithms was done manually. This process is time consuming and really depends on the medical expert’s opinion, which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining the knowledge from the patient’s clinical data. The proposed clinical decision support system for risk prediction of heart patients consists of two phases, (1) automated approach for generation of weighted fuzzy rules and decision tree rules, and, (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

Intron identification approaches based on weighted features and fuzzy decision trees

Current computational predictions of splice sites largely depend on the sequence patterns of known intronic sequence features (ISFs) described in the classical intron definition model (IDM). The computation-oriented IDM (CO-IDM) clearly provides more specific and concrete information for describing intron flanks of splice sites (IFSSs). In the paper, we proposed a novel approach of fuzzy decision trees (FDTs) which utilize (1) weighted ISFs of twelve uni-frame patterns (UFPs) and forty-five multi-frame patterns (MFPs) and (2) gain ratios to improve the performances in identifying an intron. First, we fuzzified extracted features from genomic sequences using membership functions with an unsupervised self-organizing map (SOM) technique. Then, we brought in different viewpoints of globally weighting and crossly referring in generating fuzzy rules, which are interpretable and useful for biologists to verify whether a sequence is an intron or not. Finally, the experimental results revealed the effectiveness of the proposed method in improving the identification accuracy. Besides, we also implemented an on-line intronic identifier to infer an unknown genomic sequence.

OFP_CLASS: a hybrid method to generate optimized fuzzy partitions for classification

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means of fuzzy sets, which constitute a fuzzy partition of the domains of these attributes. This method carries out a fuzzy discretization of continuous attributes in two stages. A fuzzy decision tree is used in the first stage to propose an initial set of crisp intervals, while a genetic algorithm is used in the second stage to define the membership functions and the cardinality of the partitions. After defining the fuzzy partitions, we evaluate and compare them with previously existing ones in the literature.

A Multi-Agent Memetic System for Human-Based Knowledge Selection

In these last decades, both industrial and academic organizations have used extensively different learning methods to improve humans' capabilities and, as consequence, their overall performance and competitiveness in the new economy context. However, the rapid change in modern knowledge due to exponential growth of information sources is complicating learners' activity. At the same time, new technologies offer, if used in a right way, a range of possibilities for the efficient design of learning scenarios. For that reason, novel approaches are necessary to obtain suitable learning solutions which are able to generate efficient, personalized, and flexible learning experiences. From this point of view, computational intelligence methodologies can be exploited to provide efficient and intelligent tools to be able to analyze learner's needs and preferences and, consequently, personalize its knowledge acquirement. This paper reports an attempt to achieve these results by exploiting an ontological representation of learning environment and an adaptive memetic approach, integrated into a cooperative multi-agent framework. In particular, a collection of agents analyzes learner preferences and generate high-quality learning presentations by executing, in a parallel way, different cooperating optimization strategies. This cooperation is performed by jointly exploiting data mining via fuzzy decision trees, together with a decision-making framework exploiting fuzzy methodologies.

Fuzzy SLIQ Decision Tree Based on Classification Sensitivity

The determination of membership function is fairly critical to fuzzy decision tree induction. Unfortunately, generally used heuristics, such as SLIQ, show the pathological behavior of the attribute tests at split nodes inclining to select a crisp partition. Hence, for induction of binary fuzzy tree, this paper proposes a method depending on the sensitivity degree of attributes to all classes of training examples to determine the transition region of membership function. The method, properly using the pathological characteristic of common heuristics, overcomes drawbacks of G-FDT algorithm proposed by B. Chandra, and it well remedies defects brought on by the pathological behavior. Moreover, the sensitivity degree based algorithm outperforms G-FDT algorithm in respect to classification accuracy.

A Real-time Billboard Trademark Recognition Algorithm in Sports Video

摘要: 近期, 体育视频分析中的广告牌商标的探测和识别方法已经广泛应用于许多其他领域, 比如商业电视. 基于此, 提出了一种能在不同体育视频(如足球、篮球和F1赛车等)中进行广告牌商标实时识别的算法, 该算法主要包括两个步骤, 首先, 利用基于模糊决策树的方法进行广告牌图像帧的探测; 其次, 利用颜色特征和局部SIFT (Scale-invariant feature transform)特征来描述不同商标的外观, 并最终通过基于潜在语义分析(Latent semantic analysis, LSA)的SIFT词汇匹配来识别所给定的商标模板. 初步的实验表明了本文算法的有效性, 并且该算法能在实时情况下运行. 关键词: 商标识别 / 模糊决策树 / SIFT词汇 / 潜在语义分析