Fuzzy Labels Research Articles

PELABELAN FUZZY PADA GRAF

This paper discusses fuzzy labeling graph and its properties. The properties discussed are fuzzy labeling subgraph, union graph, and fuzzy magic graph. The results showed that the strength of connectedness for pair of vertices in a graph with fuzzy labeling is always greater than or equal to the strength of connectedness for pair of vertices in its subgraph. In addition, union of two graph with fuzzy labeling will form a graph with fuzzy labeling. Meanwhile, on magic labeling there is always a fuzzy bridge on fuzzy magic graph.

Fault Detection Systems via a Novel hybrid Methodology for Fuzzy Logic Systems based on Individual base inference and Statistical Process Control

This paper presents a novel methodology to model fuzzy logic systems used as fault detection systems. This method is based in a hybrid model assembled via Individual Base Inference (IBI), Statistical Process Control (SPC) tool in the form of Control Charts (CC) and MacVicar-Whelan Methodologies. The novel method provides a simplification of the necessary calculus of inference engine module with the approaches SPC tool (CC: X-S and X-R). The inference engine is assembled via a novel methodology based in the concepts used to create CC and can be made with the specification for R charts or X-S charts. This methodology consists in the hybridization of Macvicar-Whelan method for the assignation of fuzzy labels to provide rules. The IBI is created with the wide spread of real historical data. This data forms the Universe of Discourse (UOD) and the SPC tool is used to provide the fuzzy rules and rule reduction. The aim of this paper is to test the novel method and to compare it with a classic method to prove that this method is an efficient form to rule deduction without lost reliability in the output approximation. The proposed method was tested in an electrical power system.

Novel Properties of Fuzzy Labeling Graphs

The concepts of fuzzy labeling and fuzzy magic labeling graph are introduced. Fuzzy magic labeling for some graphs like path, cycle, and star graph is defined. It is proved that every fuzzy magic graph is a fuzzy labeling graph, but the converse is not true. We have shown that the removal of a fuzzy bridge from a fuzzy magic cycle with odd nodes reduces the strength of a fuzzy magic cycle. Some properties related to fuzzy bridge and fuzzy cut node have also been discussed.

Design and Implementation of a Takagi–Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot

This paper presents a novel implementation of a Takagi-Sugeno-type fuzzy logic controller (FLC) on a two-wheeled mobile robot (2WMR), which consists of two wheels in parallel and an inverse pendulum. The control objective of the 2WMR is to achieve position control of the wheels while keeping the pendulum around the upright position that is an unstable equilibrium. The novelties of this work lie in three aspects. First, the FLC is a synthesized design which utilizes both heuristic knowledge and model information of the 2WMR system. The FLC structure, including the fuzzy labels, membership functions, and inference, is chosen based on heuristic knowledge about the 2WMR. The output parameters of the FLC are determined by comparing the output of the FLC with that of a linear controller at certain operating points, which avoids the difficulty and tediousness in manual tuning. The linear controller is designed based on a linearized model of the 2WMR system. Second, the proposed FLC is a simple and realizable design for real implementation. Only two fuzzy labels are adopted for each fuzzy variable. Sixteen fuzzy rules are used with eight output parameters and four range parameters for the membership functions to be determined. Third, the proposed FLC is successfully implemented on a real-time 2WMR for regulation and setpoint control tasks. Satisfactory responses are achieved when the 2WMR travels not only on a flat surface but also on an inclined surface. Through comprehensive experiment-based investigations, the effectiveness of the proposed FLC is validated, and the FLC shows superior performance than the existing methods.

Hybrid Bayesian estimation tree learning with discrete and fuzzy labels

Classical decision tree model is one of the classical machine learning models for its simplicity and effectiveness in applications. However, compared to the DT model, probability estimation trees (PETs) give a better estimation on class probability. In order to get a good probability estimation, we usually need large trees which are not desirable with respect to model transparency. Linguistic decision tree (LDT) is a PET model based on label semantics. Fuzzy labels are used for building the tree and each branch is associated with a probability distribution over classes. If there is no overlap between neighboring fuzzy labels, these fuzzy labels then become discrete labels and a LDT with discrete labels becomes a special case of the PET model. In this paper, two hybrid models by combining the naive Bayes classifier and PETs are proposed in order to build a model with good performance without losing too much transparency. The first model uses naive Bayes estimation given a PET, and the second model uses a set of small-sized PETs as estimators by assuming the independence between these trees. Empirical studies on discrete and fuzzy labels show that the first model outperforms the PET model at shallow depth, and the second model is equivalent to the naive Bayes and PET.

Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms.

Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling

Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary method based on time–frequency distribution and non-negative matrix factorization. The clustering algorithms including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the formulation of the proposed discriminant feature clustering method. Experiments performed with pathological speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and telemonitoring of Parkinson’s disease problems produced desirable results. The outcome demonstrates the benefits of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not exhibit a high performance.

Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification

The dynamic use of voice qualities in spoken language can reveal useful information on a speakers attitude, mood and affective states. This information may be very desirable for a range of, both input and output, speech technology applications. However, voice quality annotation of speech signals may frequently produce far from consistent labeling. Groups of annotators may disagree on the perceived voice quality, but whom should one trust or is the truth somewhere in between? The current study looks first to describe a voice quality feature set that is suitable for differentiating voice qualities on a tense to breathy dimension. Further, the study looks to include these features as inputs to a fuzzy-input fuzzy-output support vector machine (F2SVM) algorithm, which is in turn capable of softly categorizing voice quality recordings. The F2SVM is compared in a thorough analysis to standard crisp approaches and shows promising results, while outperforming for example standard support vector machines with the sole difference being that the F2SVM approach receives fuzzy label information during training. Overall, it is possible to achieve accuracies of around 90% for both speaker dependent (cross validation) and speaker independent (leave one speaker out validation) experiments. Additionally, the approach using F2SVM performs at an accuracy of 82% for a cross corpus experiment (i.e. training and testing on entirely different recording conditions) in a frame-wise analysis and of around 97% after temporally integrating over full sentences. Furthermore, the output of fuzzy measures gave performances close to that of human annotators.

Topology-Based Nonlocal Fuzzy Segmentation of Brain MR Image With Inhomogeneous and Partial Volume Intensity

The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis. Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM. The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach. Our approach can be applied to automatic segmentation of the brain MR image.

FEATURE SELECTION AND GRANULARITY LEARNING IN GENETIC FUZZY RULE-BASED CLASSIFICATION SYSTEMS FOR HIGHLY IMBALANCED DATA-SETS

This paper proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of highly imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact models by selecting the adequate variables and adapting the number of fuzzy labels for each problem, improving the interpretability of the model. The experimental analysis is carried out over a wide range of highly imbalanced data-sets and uses the statistical tests suggested in the specialized literature.

Synthesized design of a fuzzy logic controller for an underactuated unicycle

In this paper, we propose synthesized design of a fuzzy logic controller (FLC) for control of an underactuated unicycle system. The FLC objective is velocity control of the wheel while keeping the pendulum upright, which is an unstable equilibrium. The synthesized design consists of three phases. First, the FLC structures including the number of rules, membership functions, inference and parametric relations are chosen based on heuristic knowledge about the unicycle. Second, on the basis of a linearized model and linear feedback, the FLC output parameters are determined quantitatively for stabilization of the unicycle. Third, the FLC output parameters are tuned using an iterative learning tuning (ILT) algorithm, which minimizes an objective function that specifies the desired unicycle performance. The rationale for the synthesized FLC design is full utilization of the available information, which is achieved by combining model-based and model-free designs, and hence improves the FLC performance. We minimize the number of FLC rules and fuzzy labels. Six rules are used for regulation or setpoint tasks, whereas 10 rules are used with extra integral control to eliminate steady-state errors induced by system uncertainties and disturbances. Only two fuzzy labels are adopted for each fuzzy variable. The ILT process consists of two phases, exploration for stabilization and exploitation for better performance. The effectiveness of the proposed FLC is validated using intensive simulations and comparisons.

ET2FIS: An Evolving Type-2 Neural Fuzzy Inference System

There are two main approaches to design a neural fuzzy system; namely, through expert knowledge, and through numerical data. While the computational structure of a system is manually crafted by human experts in the former case, self-organizing neural fuzzy systems that are able to automatically extract generalized knowledge from batches of numerical training data are proposed for the latter. Nevertheless, both of these approaches are static where only parameters of a system are updated during training. On the other hand, the demands and complexities of real-life applications often require a neural fuzzy system to adapt both its parameters and structure to model the changing dynamics of the environment. To counter these modeling bottlenecks, intense research efforts are subsequently channeled into the studies of evolving/online neural fuzzy systems. There are generally two classes of evolving neural fuzzy systems: the Takagi–Sugeno–Kang (TSK) systems and the Mamdani systems. While most existing literature consists of evolving Type-1 TSK-typed and Type-1 Mamdani-typed models, they may not perform well in noisy environment. To improve the robustness of these neural fuzzy systems, recent efforts have been directed to extend evolving Type-1 TSK-typed neural fuzzy systems to Type-2 models because of their better known noise resistance abilities. In contrast, minimum similar effort has been made for evolving Mamdani-typed models. In this paper, we present a novel evolving Type-2 Mamdani-typed neural fuzzy system to bridge this gap. The proposed system is named evolving Type-2 neural fuzzy inference system (eT2FIS), and it employs a data-driven incremental learning scheme. Issues involving the online sequential learning of the eT2FIS model are carefully examined. A new rule is created when a newly arrived data is novel to the present knowledge encrypted; and an obsolete rule is deleted when it is no longer relevant to the current environment. Highly over-lapping fuzzy labels in the input–output spaces are merged to reduce the computational complexity and improve the overall interpretability of the system. By combining these three operations, eT2FIS is ensured a compact and up-to-date fuzzy rule base that is able to model the current underlying dynamics of the environment. Subsequently, the proposed eT2FIS model is employed in a series of benchmark and real-world applications to demonstrate its efficiency as an evolving neural fuzzy system, and encouraging performances have been achieved.

LFTM, linguistic fuzzy trust mechanism for distributed networks

SUMMARYTrust is, in some cases, being considered as a requirement in highly distributed communication scenarios. Before accessing a particular service, a trust model is then being used in these scenarios to determine if the service provider can be trusted or not. It is done usually on behalf of the final user or service customer, and with a little intervention of him or her. This is usually happening with the main aim of automatizing the process and because trust models are normally making use of reasoning mechanisms and models difficult to understand by humans. In this paper, we propose the adaptation of a bio‐inspired trust model to deal with linguistic fuzzy labels, which are closer to the human way of thinking. This Linguistic Fuzzy Trust Model also uses fuzzy reasoning. Results show that the new model keeps the accuracy of the underlying bio‐inspired trust model and the level of client satisfaction, while enhancing the interpretability of the model and thus making it closer to the final user. Copyright © 2011 John Wiley & Sons, Ltd.

A transduction-based approach to fuzzy clustering, relevance ranking and cluster label generation on web search results

This paper details a modular, self-contained web search results clustering system that enhances search results by (i) performing clustering on lists of web documents returned by queries to search e...

Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.

Motion tracking using fuzzy logic and consistent labeling for multiple objects in multiple cameras vision

In this work we address the issue of tracking multiple objects in an environment covered by multiple cameras having overlapped fields of view (FOV); we use FOV lines to achieve consistent labeling. The proposed method results advantageous in video-surveillance of indoor and outdoor environments (e.g., airports, metropolitan areas, banks, etc.) or in cinematic and television field for the optimal camera shooting management, suggesting the camera that offers the best view of the objects in the scene. We propose a system infrastructure and a new approach based on a Hierarchical Multilevel Clusterized Architecture (HMC) applied to image tracking. The areas that two or more fields of view have in common are the core for our approach: thanks to FOV lines detection, a Consistent Labeling mechanism is achieved with the use of projective invariants. The user has the chance to indicate one or more Interest Areas. Object Tracking is performed with the application of two tracking algorithms in parallel, ensuring efficient solutions even in scenarios with noised images. The system is time continuous: the temporal distance between two consecutive DAQ frames is much smaller than the one between two consecutive tracking frames. The Human Behaviour Analysis (HBA) Module avails itself of a Decision Support System (DSS) to guide the user in those critical situations that can occur while observing the scene. The anomaly detection is executed with the use of optional ribbons; collisions are managed through the use of the color histograms of the ribbons representing the object. The black areas management (i.e., uncovered areas) is performed by the Fuzzy Prediction System (FPS), structured on two levels and capable to predict the position of the tracked object in the instants immediately subsequent to its entrance in a black area, by means of fuzzy inference rules. The system results particularly efficient thanks to the implementation of a Consistent Labeling mechanism, united with a FPS that contributes to more and more accurate tracking. The use of a DSS and a HBA module simplifies the usage of the system, preserving its security issues.

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.

Fusion of classic P300 detection methods’ inferences in a framework of fuzzy labels

Designing a reliable and accurate brain-computer interface (BCI) is one of the most challenging fields in biomedical signal processing. To achieve this goal, different methods have been adopted in different blocks of a typical BCI system (i.e., in preprocessing, feature extraction, feature classification and feature selection blocks). Since BCI's speed plays a crucial role in its success in real-life applications, using mathematically simple techniques with accurate and reliable performance can improve this aspect of BCI systems' design. In this paper, a new method is introduced, which combines information from different classic time series similarity measures, using a simple fuzzy fusion framework. This method is accurate and reliable in P300 (a positive event-related component occurring 300 ms after stimulus onset) detection. This framework is used to combine two computationally simple signal detection methods: "peak picking" and "template matching". Fusion takes place in the last step (decision-making step) by means of a fuzzy rule-base. Compared to similar works on electroencephalogram-based (EEG-based) BCI datasets, in spite of being computationally simple, this new technique's performance is comparable to very complicated methods, like support vector machines. This research indicates that, using both spatial and temporal information content of EEG trials (from all electrodes or a subset of them), even under a non-complicated mathematical framework can yield an accurate and powerful classification.

Fuzzy classification using information theoretic learning vector quantization

In this article we extend the (recently published) unsupervised information theoretic vector quantization approach based on the Cauchy–Schwarz-divergence for matching data and prototype densities to supervised learning and classification. In particular, first we generalize the unsupervised method to more general metrics instead of the Euclidean, as it was used in the original algorithm. Thereafter, we extend the model to a supervised learning method resulting in a fuzzy classification algorithm. Thereby, we allow fuzzy labels for both, data and prototypes. Finally, we transfer the idea of relevance learning for metric adaptation known from learning vector quantization to the new approach. We show the abilities and the power of the method for exemplary and real-world medical applications.

A new fuzzy approach for handling class labels in canonical correlation analysis

Canonical correlation analysis (CCA) can extract more discriminative features by utilizing class labels, especially the ones that can reflect the sample distribution appropriately. In this paper, a new fuzzy approach for handling class labels in the form of fuzzy membership degrees is proposed. We elaborately design a novel fuzzy membership function to represent the distribution of image samples. These fuzzy class labels promote the classification performances of CCA and kernel CCA (KCCA) through incorporating distribution information into the process of feature extraction. Comprehensive experimental results on face recognition demonstrate the effectiveness and feasibility of the proposed method.