Fuzzy Rule-based Classification Systems Research Articles

Finding Intensity of Disease by Fuzzy Learning Rules (FLR) based Intelligent Decisive Support System for Efficient Disease Prediction

Experimental analysis is frequently done by doctor‟s expertise and involvement. But still, cases are testified of incorrect diagnosis and action. Patients are demanded to take some tests for examination. In many belongings, not all the tests contribute towards efficient analysis of a disease. Three classifiers similar Naive Bayes, Ordering by clustering and decision tree are used to calculate the analysis of patients with the same accuracy as acquired before the discount of some attributes. Fuzzy learning rules (FLR) are generally applied for finding the intensity of disease in data sets. Fuzzy learning rules better compared to other three methods. We propose a fuzzy learning algorithm to determine relationships between data resources based on their disease attributes, as well as to characterize knowledge through the connotation of disease covered by those properties. The algorithm addresses the significant problem of important a suitable number of clusters for suitably catching all the diseases of the knowledge domain. Using fuzzy rule-based classification system, the proposed system proves to improve the classification accuracy.

A technical note on the paper “hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1]. They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model.

On fuzzy feature selection in designing fuzzy classifiers for high-dimensional data

Generating fuzzy rules for high-dimensional data has been a serious challenge in designing fuzzy rule-based classification systems. For data sets with low dimensions, there are some efficient methods to generate a compact set of short fuzzy rules. However, when the dimensions go up, the number of rules increases exponentially. One solution for lowering the dimensions is feature selection which selects a subset of more effective features. In this regard, a fuzzy feature selection approach is proposed in this paper which tries to choose more relevant features; those which can distinguish the distinct classes well. Our method employs the training patterns in the subspace of some predefined fuzzy sets on each feature and applies their compatibility degrees to evaluate that feature. Since each feature is evaluated individually, this method can be applied efficiently on high-dimensional data. Using the selected features to generate rules in fuzzy rule-based classifiers, this paper also presents a novel criterion to assess each generated rule. This criterion measures the capability of each fuzzy rule in discriminating the positive and negative patterns. To illustrate the scalability of our fuzzy feature selection method beside to the efficiency of generated fuzzy rules, they are applied on some benchmark data sets and the results are compared to some other methods in the literature. The experimental results justify the feasibility of our approach to work with high-dimensional data and its acceptable performance in terms of designing CPU time and classification accuracy.

Fuzzy rule weight modification with particle swarm optimisation

The most challenging problem in developing fuzzy rule-based classification systems is the construction of a fuzzy rule base for the target problem. In many practical applications, fuzzy sets that are of particular linguistic meanings, are often predefined by domain experts and required to be maintained in order to ensure interpretability of any subsequent inference results. However, learning fuzzy rules using fixed fuzzy quantity space without any qualification will restrict the accuracy of the resulting rules. Fortunately, adjusting the weights of fuzzy rules can help improve classification accuracy without degrading the interpretability. There have been different proposals for fuzzy rule weight tuning through the use of various heuristics with limited success. This paper proposes an alternative approach using Particle Swarm Optimisation in the search of a set of optimal rule weights, entailing high classification accuracy. Systematic experimental studies are carried out using common benchmark data sets, in comparison to popular rule based learning classifiers. The results demonstrate that the proposed approach can boost classification performance, especially when the size of the initially built rule base is relatively small, and is competitive to popular rule-based learning classifiers.

Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method

Multi-class classification problems appear in a broad variety of real-world problems, e.g., medicine, genomics, bioinformatics, or computer vision. In this context, decomposition strategies are useful to increase the classification performance of classifiers. For this reason, in a previous work we proposed to improve the performance of FARC-HD (Fuzzy Association Rule-based Classification model for High-Dimensional problems) fuzzy classifier using One-vs-One (OVO) and One-vs-All (OVA) decomposition strategies. As a result of an exhaustive experimental analysis, we concluded that even though the usage of decomposition strategies was worth to be considered, further improvements could be achieved by introducing n-dimensional overlap functions instead of the product t-norm in the Fuzzy Reasoning Method (FRM). In this way, we can improve confidences for the subsequent processing performed in both OVO and OVA.In this paper, we want to conduct a broader study of the influence of the usage of n-dimensional overlap functions to model the conjunction in several Fuzzy Rule-Based Classification Systems (FRBCSs) in order to enhance their performance in multi-class classification problems applying decomposition techniques. To do so, we adapt the FRM of four well-known FRBCSs (CHI, SLAVE, FURIA, and FARC-HD itself). We will show that the benefits of the usage of n-dimensional overlap functions strongly depend on both the learning algorithm and the rule structure of each classifier, which explains why FARC-HD is the most suitable one for the usage of these functions.

Rule-based fuzzy classifier based on quantum ant optimization algorithm

Fuzzy rule-based classification systems have been used extensively in data mining. This paper proposes a fuzzy rule- based classification algorithm based on a quantum ant optimization algorithm. A method of generating the hierarchical rules with different granularity hybridization is used to generate the initial rule set. This method can obtain an original rule set with a smaller number of rules. The modified quantum ant optimization algorithm is used to generate the optimal individual. Compared to other similar algorithms, the algorithm proposed in this paper demonstrates higher classification accuracy and a higher convergence rate. The algorithm is proved to be convergent on theory. Some experiments have been conducted on the algorithm, and the results proved that the algorithm is feasible.

A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data

The current financial crisis has stressed the need to obtain more accurate prediction models in order to decrease risk when investing money on economic opportunities. In addition, the transparency of the process followed to make the decisions in financial applications is becoming an important issue. Furthermore, there is a need to handle real-world imbalanced financial datasets without using sampling techniques that might introduce noise in the used data. In this paper, we present a compact evolutionary interval-valued fuzzy rule-based classification system, which is based on interval-valued fuzzy rule-based classification system with tuning and rule selection (IVTURS $_{\rm FARC{\hbox{-}}HD}$ ) for the modeling and prediction of real-world financial applications. This proposed system allows obtaining good prediction accuracies using a small set of short fuzzy rules implying a high degree of interpretability of the generated linguistic model. Furthermore, the proposed system deals with the financial imbalanced datasets with no need for any preprocessing or sampling method and, thus, avoiding the accidental introduction of noise in the data used in the learning process. The system is also provided with a mechanism to handle examples that are not covered by any fuzzy rule in the generated rule base. To test the quality of our proposal, we will present an experimental study including 11 real-world financial datasets. We will show that the proposed system outperforms the original C4.5 decision tree, type-1, and interval-valued fuzzy counterparts that use the synthetic minority oversampling technique (SMOTE) to preprocess data and the original FURIA, which is a fuzzy approximative classifier. Furthermore, the proposed method enhances the results achieved by the cost-sensitive C4.5, and it gives competitive results when compared with FURIA using SMOTE, while our proposal avoids preprocessing techniques, and it provides interpretable models that allow obtaining more accurate results.

Fuzzy classification of pre-harvest tomatoes for ripeness estimation – An approach based on automatic rule learning using decision tree

Tomato (Solanum lycopersicum) ripeness estimation is an important process that affects its quality evaluation and marketing. However, the slow speed, subjectivity, time consumption associated with manual assessment has been forcing the agriculture industry to apply automation through robots. The vision system of harvesting robot is responsible for two-tasks. The first task is the recognition of object (tomato) and second is the classification of recognized objects (tomatoes). In this paper, Fuzzy Rule-Based Classification approach (FRBCS) has been proposed to estimate the ripeness of tomatoes based on color. The two color depictions: red-green color difference and red-green color ratio are derived from extracted RGB color information. These are then compared as a criterion for classification. Fuzzy partitioning of the feature space into linguistic variables is done by means of a learning algorithm. A rule set is automatically generated from the derived feature set using Decision Trees. Mamdani fuzzy inference system is adopted for building the fuzzy rule based classification system that classifies the tomatoes into six maturity stages. Dataset used for experiments has been created using the real images that were collected from a farm. 70% of the total images were used for training and 30% images of the total were used for testing the dataset respectively. Training dataset is divided into six classes representing the six different stages of tomato ripeness. Experimental results showed the system achieved the ripeness classification accuracy of 94.29% using proposed FRBCS.

Sensitivity analysis of fuzzy rule-based classification systems by means of the Lipschitz condition

The fuzzy rule-based classifier can be taken as a function that assigns to a point from the feature space a class, or a class with an association degree. Under this assumption, the robustness of fuzzy rule-based classifiers is investigated by means of the Lipschitz condition. The Lipschitz continuity of fuzzy sets, fuzzy rules and whole fuzzy rule-based classifiers is examined for multi-polar outputs, extended multi-polar outputs and outputs in the form of a class. Related performance of a fuzzy rule-based classifier is also discussed. All studied concepts are shown on an exemplar fuzzy rule-based classifier.

Belief rule-based classification system: Extension of FRBCS in belief functions framework

Among the computational intelligence techniques employed to solve classification problems, the fuzzy rule-based classification system (FRBCS) is a popular tool capable of building a linguistic model interpretable to users. However, it may face lack of accuracy in some complex applications, by the fact that the inflexibility of the concept of the linguistic variable imposes hard restrictions on the fuzzy rule structure. In this paper, we extend the fuzzy rule in FRBCS with a belief rule structure and develop a belief rule-based classification system (BRBCS) to address imprecise or incomplete information in complex classification problems. The two components of the proposed BRBCS, i.e., the belief rule base (BRB) and the belief reasoning method (BRM), are designed specifically by taking into account the pattern noise that existes in many real-world data sets. Four experiments based on benchmark data sets are carried out to evaluate the classification accuracy, robustness, interpretability and time complexity of the proposed method.

Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a Cluster-Based Class Representation

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules.

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.

The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certainty CF j of each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.

A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules

AbstractThe big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification system that uses the MapReduce framework to learn and fuse rule bases. It has been developed in two versions with different fusion processes. An experimental study is carried out and the results obtained show that the proposal is able to handle these problems providing competitive results.

Multipolar Aggregation Operators in Reasoning Methods for Fuzzy Rule-Based Classification Systems

The reasoning methods in fuzzy rule-based classification systems are studied, and their relation to multipolar aggregation operators is discussed. We describe the change in the output of the classification system based on the change of the fuzzy reasoning method. We propose a new fuzzy reasoning method based on multipolar fusion OWA operator which can control the transition between the maximum rule method and the maximum vote method by a weighting vector. This new method uses principles of OWA operators and assigns the rule weight according to the strength of activation of the rule. We compare different fuzzy reasoning methods in case studies.

Genetic fuzzy rule based classification systems for coronary plaque characterization based on intravascular ultrasound images

Vascular tissue characterization is of great importance concerning the possibility of an Acute Cardiac Syndrome (ACS). Gray-scale intravascular ultrasound (IVUS) is a powerful tomographic modality providing a thorough visualization of coronary arteries. Among the existing methods, virtual histology (VH) is the most popular and clinically available technique for plaque component analysis. IVUS-VH though suffers from a poor longitudinal resolution due to the electrocardiogram (ECG)-gated frame acquisition. In order to surmount this demerit, a new image-based methodology for plaque assessment is suggested in this paper that differentiates tissue components into four classes: calcium, necrotic core, fibrous and fibro-lipid. The available IVUS frames characterized by VH are used for plaque labeling. In addition, a rich set of five textural feature families are extracted from IVUS images and computed at different scales. To increase the discrimination capabilities between the classes, all features are combined into an integrated feature vector. The classification of plaque types is accomplished using a genetic fuzzy rule-based classification system (GFRBCS) that is able to handle effectively highly-dimensional feature spaces. The incorporated feature selection mechanism along with the rule extraction algorithm allows the creation of compact fuzzy rule bases with enhanced classification accuracy. The high interpretability properties of our classifier assist physicians to gain a deeper insight regarding the impact of features involved in the evaluation of atherosclerotic plaques. An extensive experimental analysis is carried out, where various tests are performed in order to highlight the advantages of the proposed scheme against existing methods of the literature, including other GRBCSs and the support vector machine (SVM) classifier. In particular, the attained results show that the applied classifier exhibits better generalization capabilities than the competing methods, i.e., higher accuracy in characterizing unseen IVUS images.

Performance analysis of fuzzy rule based classification system for transient identification in nuclear power plant

Even though fuzzy rule based classification system (FRBCS) has been useful in event identification, it has led to strong clash in terms of better interpretability along with adequate percentage of accuracy. Basically for classification in nuclear power plant (NPP) which receives data within a cycle time of few milliseconds, either the accuracy or the interpretability of the FRBCS would get jeopardized. Online event identification of any abnormality or transient using FRBCS becomes really critical for the plant which has such a short cycle time. For such cases, the output from a FRBCS may not be conducive to classify the event every cycle. Thus, it is necessary to monitor the output of a classification system for certain amount of cycles till the static nature is attained. This gives a high level of confidence on the classifier output to be accurate. A FRBCS can produce this level of confidence by choosing the best input features with high interpretability and acceptable accuracy. The best feature selection out of a lot of input variables and preparing the rule base is again a very critical and challenging task in FRBCS. There is always a dilemma on judiciously choosing the number of input features for the FRBCS to achieve an optimized interpretable and accurate fuzzy system. It is always advisable to select least number of features with proper output error margin for a FRBCS. On adding extra features along with some rules as input to the system, certainly increases the accuracy of the system but degrades its interpretability. The other thing which is of equal importance is to keep an agile notice on the output which has to be within the output error margin. The output error margin denotes the response time and static nature of the classifier output. Here, the classification could be confirmed only after observing the output for a subsequent number of cycles. Both the number of inputs and the output error margin needs to be properly balanced in order to get an efficient online FRBCS for such kind of low cycle time systems. This paper concentrates on the performance analysis of some FRBCS with different number of input features and their behavior towards different datasets from Prototype Fast Breeder Reactor (PFBR) which is a NPP. Based on the PFBR Operator Training Simulator (OTS) data, some FRBCS are developed to identify some of the transients or abnormalities occurring in PFBR. The comparison is done based on the number of input features, response time and static nature of the output in each of the produced systems.

A multi-objective evolutionary method for learning granularities based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracy-interpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning process and a rule selection process can be combined to obtain a set of solutions with different trade-offs between the accuracy and the compactness of models. Nevertheless, an initial model needs to be defined, in particular the parameters that describe the partitions and the number of fuzzy sets of each variable (i.e. the granularities) must be determined. The simplest approach is to use a previously established single granularity and a uniform fuzzy partition for each variable. A better approach consists in automatically identifying from data the appropriate granularities and fuzzy partitions, since this usually leads to more accurate models.This contribution presents a fuzzy discretization approach, which is used to generate automatically promising granularities and their associated fuzzy partitions. This mechanism is integrated within a Multi-Objective Fuzzy Association Rule-Based Classification method, namely D-MOFARC, which concurrently performs a tuning and a rule selection process on an initial knowledge base. The aim is to obtain fuzzy rule-based classification systems with high classification performances, while preserving their complexity.

A Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems

Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the proposed methods by now, may over-fit on training data due to generating complex decision boundaries. In this paper, a margin-based optimization model is proposed to improve the performance on unseen data. By this model, fixed-size margins are defined along the decision boundaries and the rule weights are adjusted such that the marginal space would be empty of training instances as much as possible. This model is proposed to support the single-winner reasoning method with a special cost-function to remove undesired effects of noisy instances. The model is proposed to be solved by a fast well-known local search method. With this solving method, a huge amount of irrelevant and redundant rules are removed as a side effect.Two artificial and 16 real world datasets from UCI repository are used to show that the proposed method significantly outperforms other methods with proper choice of the margin size, which is the single parameter of this method.

Averaging operators in fuzzy classification systems

The averaging operators used for aggregation of outputs of fuzzy classification rules are examined. Several methods used in fuzzy rule-based classification systems are discussed and related multi-polar averaging operators are shown. Further, new aggregation methods based on the idea used for the definition of OWA operators are described. The connection between multi-polar averaging operators and the multi-polar Choquet integral is shown and using this connection the conditions under which are the respective averaging operators monotone are studied. Several examples of special multi-polar OWA operators with a relation to existing bipolar aggregation operators are included.

Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data

Classification with big data has become one of the latest trends when talking about learning from the available information. The data growth in the last years has rocketed the interest in effectively acquiring knowledge to analyze and predict trends. The variety and veracity that are related to big data introduce a degree of uncertainty that has to be handled in addition to the volume and velocity requirements. This data usually also presents what is known as the problem of classification with imbalanced datasets, a class distribution where the most important concepts to be learned are presented by a negligible number of examples in relation to the number of examples from the other classes. In order to adequately deal with imbalanced big data we propose the Chi-FRBCS-BigDataCS algorithm, a fuzzy rule based classification system that is able to deal with the uncertainly that is introduced in large volumes of data without disregarding the learning in the underrepresented class. The method uses the MapReduce framework to distribute the computational operations of the fuzzy model while it includes cost-sensitive learning techniques in its design to address the imbalance that is present in the data. The good performance of this approach is supported by the experimental analysis that is carried out over twenty-four imbalanced big data cases of study. The results obtained show that the proposal is able to handle these problems obtaining competitive results both in the classification performance of the model and the time needed for the computation.