Fuzzy Rule-based Classification Research Articles

Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion

Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness in the world. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of blood vessels of the retina. In this work, the performance of descriptive statistical features in retinal vessel segmentation is evaluated by using fuzzy logic, an artificial neural network classifier (ANN), a support vector machine (SVM), and classifier fusion. Newly constructed eight features are formed by statistical moments. Mean and median measurements of image pixels’ intensity values in four directions, horizontal, vertical, up-diagonal, and down-diagonal, are calculated. Features, F1, F2, F3, and F4 are calculated as the mean values and F5, F6, F7, and F8 are calculated as the median values of a processed pixel in each direction. A fuzzy rule-based classifier, an ANN, a SVM, and a classifier fusion are designed. The publicly available DRIVE and STARE databases are used for evaluation. The fuzzy classifier achieved 93.82% of an overall accuracy, 72.28% of sensitivity, and 97.04% of specificity. For the ANN classifier, 94.2% of overall accuracy, 67.7% of sensitivity, and 98.1% of specificity are achieved on the DRIVE database. For the STARE database, the fuzzy classifier achieved 92.4% of overall accuracy, 75% of sensitivity, and 94.3% of specificity. The ANN classifier achieved the overall accuracy, sensitivity, and specificity as 94.2%, 56.9%, and 98.4%, respectively. Although the overall accuracy of the SVM is calculated lower than the fuzzy and the ANN classifiers, it achieved higher sensitivity rates. Designed classifier fusion achieved the best performance among all by using the proposed statistical features. Its overall accuracy, sensitivity, and specificity are calculated as 95.10%, 74.09%, 98.35% for the DRIVE and 95.53%, 70.14%, 98.46 for the STARE database, respectively. The experimental results validate that the descriptive statistical features can be employed in retinal vessel segmentation and can be used in rule-based and supervised classifiers.

Interpretable and accurate medical data classification – a multi-objective genetic-fuzzy optimization approach

In medical decision support systems, both the accuracy (i.e., the ability to adequately represent the decision making processes) as well as the transparency and interpretability (i.e., the ability to provide a domain user with compact and understandable explanation and justification of the proposed decisions) play essential roles. This paper presents an approach for automatic design of fuzzy rule-based classification systems (FRBCSs) from medical data using multi-objective evolutionary optimization algorithms (MOEOAs). Our approach generates, in a single run, a collection of solutions (medical FRBCSs) characterized by various levels of accuracy-interpretability trade-off. We propose a new complexity-related interpretability measure and we address the semantics-related interpretability issue by means of efficient implementation of the so-called strong fuzzy partitions of attribute domains. We also introduce a special-coding-free representation of the rule base and original genetic operators for its processing as well as we implement our ideas in the context of well-known and one of the presently most advanced MOEOAs, i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II). An important part of the paper is devoted to a broad comparative analysis of our approach and as many as 26 alternative techniques arranged in 32 experimental set-ups and applied to three well-known benchmark medical data sets (Breast Cancer Wisconsin (Original), Pima Indians Diabetes, and Heart Disease (Cleveland)) available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). A number of useful in medical applications performance measures including accuracy, sensitivity, specificity, and several interpretability measures are employed. The results of such a broad comparative analysis demonstrate that our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained FRBCSs while remaining either competitive or superior in terms of their accuracy. It is worth stressing that the overwhelming majority of the existing medical classification methods concentrate almost exclusively on the accuracy issues.

Automatic Airport Recognition Based on Saliency Detection and Semantic Information

Effectively identifying an airport from satellite and aerial imagery is a challenging task. Traditional methods mainly focus on the use of multiple features for the detection of runways and some also adapt knowledge of airports, but the results are unsatisfactory and the usage limited. A new method is proposed to recognize airports from high-resolution optical images. This method involves the analysis of the saliency distribution and the use of fuzzy rule-based classification. First, a number of images with and without airports are segmented into multiple scales to obtain a saliency distribution map that best highlights the saliency distinction between airports and other objects. Then, on the basis of the segmentation result and the structural information of airports, we analyze the segmentation result to extract and represent the semantic information of each image via the bag-of-visual-words (BOVW) model. The image correlation degree is combined with the BOVW model and fractal dimension calculation to make a more complete description of the airports and to carry out preliminary classification. Finally, the support vector machine (SVM) is adopted for detailed classification to classify the remaining imagery. The experiment shows that the proposed method achieves a precision of 89.47% and a recall of 90.67% and performs better than other state of the art methods on precision and recall.

An integer programming approach for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) have been successfully employed as a data mining technique where the goal is to discover the hidden knowledge in a data set in the form of interpretable rules and develop an accurate classification model. In this paper, we propose an exact approach to learn fuzzy rules from a data set for a FRBCS. First, we propose a mixed integer programming model that extracts optimal fuzzy rules from a data set. The model’s embedded feature selection allows absence of insignificant features in a fuzzy rule in order to enhance its accuracy and coverage. In order to build a comprehensive Rule Base (RB), we use this model in an iterative procedure that finds multiple rules by converting the obtained optimal solutions into a set of taboo constraints that prevents the model from re-finding the previously obtained rules. Furthermore, it changes the search direction by temporarily removing the correctly predicted patterns from the training set aiming to find the optimal rules that predict uncovered patterns in the training set. This procedure ensures that most of the patterns in the training set are covered by the RB. Next, another mixed integer programming model is developed to maximize predictive accuracy of the classifier by pruning the RB and removing redundant rules. The predictive accuracy of the proposed model is tested on the benchmark data sets and compared with the state-of-the-art algorithms from the literature by non-parametric statistical tests.

DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems

In this paper a novel Genetic Fuzzy Rule-based Classification System, named DECO3R (Differential Evolution based Cooperative and Competing learning of Compact FRBCS), is proposed. DECO3R follows the genetic cooperative - competitive learning (GCCL) approach and uses Differential Evolution as its learning algorithm. In this frame, every chromosome encodes a single fuzzy rule. The proposed AdaBoost-based Fuzzy Token Competition (FTC) method is employed to deal with the cooperation - competition problem, an integral part to all GCCL algorithms. DECO3R learns clear, precise and predictive rules where the fuzzy sets in the premise part are consecutive. The experimental component analysis demonstrates that DE as a learning algorithm outperforms a simple Genetic Algorithm. Additionally, the novel FTC method exceeds the performance of other similar techniques. The experimental comparative analysis highlights the robust performance of DECO3R compared to other rule learning algorithms, both in terms of accuracy and of structural complexity.

Preaggregation Functions: Construction and an Application

In this paper, we introduce the notion of preaggregation function. Such a function satisfies the same boundary conditions as an aggregation function, but, instead of requiring monotonicity, only monotonicity along some fixed direction (directional monotonicity) is required. We present some examples of such functions. We propose three different methods to build preaggregation functions. We experimentally show that in fuzzy rule-based classification systems, when we use one of these methods, namely, the one based on the use of the Choquet integral replacing the product by other aggregation functions, if we consider the minimum or the Hamacher product t-norms for such construction, we improve the results obtained when applying the fuzzy reasoning methods obtained using two classical averaging operators such as the maximum and the Choquet integral.

Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS

In this paper, we present a novel classifier ensemble method, namely Rotation Forest fuzzy rule-based Classifier Ensemble (RFCE), for spatial prediction of landslides. An area located in the Uttarakhand State in India has been taken as a case study. RFCE is a hybrid method of rotation forest ensemble and fuzzy unordered rules induction algorithm classifier. Both are the current state-of-the-art techniques which have been seldom used for spatial prediction of landslides. A dataset has been generated for this study including landslide inventory map (930 landslide locations) and fifteen landslide influencing factors such as slope angle, slope aspect, elevation curvature, plan curvature, profile curvature, soil types, land cover, rainfall, distance to lineaments, distance to roads, distance to rivers, lineament density, road density, and river density. Performance of the RFCE model has been evaluated using statistical index-based evaluations and receiver operating characteristic curve technique. Additionally, other ensemble techniques, such as AdaBoost, Bagging, MultiBoost, and other classifiers, namely Support Vector Machines (SVM) and Fuzzy Unordered Rules Induction Algorithm (FURIA), have been taken into account for comparison. Overall, the performance of the RFCE model is well within spatial prediction of landslides (AUC = 0.896). It improved significantly the predictive capability of a base classifier of the FURIA model (AUC = 0.865). Its performance is even better than the AdaBoost model (AUC = 0.874) and almost same as the MultiBoost model (AUC = 0.901) and the SVM model (AUC = 0.901). However, it falls behind the Bagging model (AUC = 0.912). The present study indicates that RFCE is an encouraging method that can be used for spatial prediction of landslides.

Capacities and overlap indexes with an application in fuzzy rule-based classification systems

In this work, we introduce a method for constructing capacities using overlap indexes between the fuzzy sets which are generated from the inputs of the considered problem. We also use these capacities to aggregate information by means of the Choquet integral in a fuzzy rule-based classifier. We observe that with these capacities the obtained results are better than those obtained with other measures.

Review of Fuzzy Rule Based Classification systems

Fuzzy Rule Based Classification systems (FRBCs) have received significant attention among the researchers due to the good behaviour in the real time databases. An important issue in the design of fuzzy rule-based classification system is the optimized generation of fuzzy if-then rules and the membership functions. The inductive learning of fuzzy rule classifier suffers in rule generation and rule optimization when the search space or variables becomes high. This creates the new idea of making the fuzzy system with precise rules leading to less scalability and improved accuracy. Accordingly, different approaches have been presented in the literature for optimal finding of fuzzy rules using optimization algorithms. Among the different techniques available in the literature, choosing the type, number of membership functions and defining parameters of membership function are still challenging tasks. In this paper, the optimization algorithms for optimal design of membership function and optimal rule generation are reviewed.

Detection of SQL Injection Using a Genetic Fuzzy Classifier System

SQL Injection (SQLI) is one of the most popular vulnerabilities of web applications. The consequences of SQL injection attack include the possibility of stealing sensitive information or bypassing authentication procedures. SQL injection attacks have different forms and variations. One difficulty in detecting malicious attacks is that such attacks do not have a specific pattern. A new fuzzy rule-based classification system (FBRCS) can tackle the requirements of the current stage of security measures. This paper proposes a genetic fuzzy system for detection of SQLI where not only the accuracy is a priority, but also the learning and the flexibility of the obtained rules. To create the rules having high generalization capabilities, our algorithm builds on initial rules, data-dependent parameters, and an enhancing function that modifies the rule evaluation measures. The enhancing function helps to assess the candidate rules more effectively based on decision subspace. The proposed system has been evaluated using a number of well-known data sets. Results show a significant enhancement in the detection procedure

An efficient multi relational framework using fuzzy rule-based classification technique

Multi relational classification is one of the rapidly rising subclasses of multi relational data mining. It focuses on identifying attractive patterns in an interconnected multiple database relations via primary key/foreign key relationship. Many conventional rule-based classification approaches produce the rules from single 'flat' relation and typically suffer from rule clashes. However, many real world data repositories are stored in multiple database relations. Owing to the increase in the quantity of relational data that are being accumulated and the limitation of propositional rule-based classifiers in relational domains, multi relational classification has turned out to be a field with significance. In this work, a multi relational fuzzy rule system (MRFRS) is proposed for classification across multiple database relations based on PART algorithm that generates crisp rules. These rules are used to formulate the multi relational fuzzy rule system that produces rules of mutually exclusive and exhaustive in nature. The empirical results show that the proposed method outperforms well on real datasets in terms of efficiency and accuracy.

An efficient multi relational framework using fuzzy rule-based classification technique

An efficient multi relational framework using fuzzy rule-based classification technique

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.

A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability

Credit classification is an important component of critical financial decision making tasks such as credit scoring and bankruptcy prediction. Credit classification methods are usually evaluated in terms of their accuracy, interpretability, and computational efficiency. In this paper, we propose an approach for automatic designing of fuzzy rule-based classifiers (FRBCs) from financial data using multi-objective evolutionary optimization algorithms (MOEOAs). Our method generates, in a single experiment, an optimized collection of solutions (financial FRBCs) characterized by various levels of accuracy-interpretability trade-off. In our approach we address the complexity- and semantics-related interpretability issues, we introduce original genetic operators for the classifier's rule base processing, and we implement our ideas in the context of Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the presently most advanced MOEOAs. A significant part of the paper is devoted to an extensive comparative analysis of our approach and 24 alternative methods applied to three standard financial benchmark data sets, i.e., Statlog (Australian Credit Approval), Statlog (German Credit Approval), and Credit Approval (also referred to as Japanese Credit) sets available from the UCI repository of machine learning databases (http://archive.ics.uci.edu/ml). Several performance measures including accuracy, sensitivity, specificity, and some number of interpretability measures are employed in order to evaluate the obtained systems. Our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained financial data classifiers while remaining either competitive or superior in terms of their accuracy and the speed of decision making.

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.

Fuzzy rule-based faulty node classification and management scheme for large scale wireless sensor networks

In a wireless sensor network (WSNs), probability of node failure rises with increase in number of sensor nodes within the network. The, quality of service (QoS) of WSNs is highly affected by the faulty sensor nodes. If faulty sensor nodes can be detected and reused for network operation, QoS of WSNs can be improved and will be sustainable throughout the monitoring period. The faulty nodes in the deployed WSN are crucial to detect due to its improvisational nature and invisibility of internal running status. Furthermore, most of the traditional fault detection methods in WSNs do not consider the uncertainties that are inherited in the WSN environment during the fault diagnosis period. Resulting traditional fault detection methods suffer from low detection accuracy and poor performance. To address these issues, we propose a fuzzy rule-based faulty node classification and management scheme for WSNs that can detect and reuse faulty sensor nodes according to their fault status. In order to overcome uncertainties that are inherited in the WSN environment, a fuzzy logic based method is utilized. Fuzzy interface engine categorizes different nodes according to the chosen membership function and the defuzzifier generates a non-fuzzy control to retrieve the various types of nodes. In addition, we employed a routing scheme that reuses the retrieved faulty nodes during the data routing process. We performed extensive experiments on the proposed scheme using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the effectiveness of the proposed algorithm in terms of various important performance metrics.