Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

Combining boosting and evolutionary algorithms for learning of fuzzy classification rules

We examine the performance of compact fuzzy rule-based classification systems that consist of a small number of simple fuzzy rules with high comprehensibility. Those fuzzy systems are designed in a heuristic manner using rule selection criteria. We first describe fuzzy rule-based classification. Next we describe heuristic rule selection criteria using the terminology in data mining: confidence and support. A small number of fuzzy rules are extracted from numerical data based on each rule selection criterion. Then we examine the classification performance of extracted fuzzy rules through computational experiments on a number of benchmark data sets from the UCI ML Repository. Our results should be viewed as the lowest benchmark performance of fuzzy rule-based classification systems because fuzzy rules are extracted using a simple heuristic method with no optimization or tuning procedures. Nevertheless our results on some data sets are comparable to reported results by the C4.5 algorithm in the literature.

RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease

A genetic algorithm for generating fuzzy classification rules

We have been using fuzzy data mining techniques to extract patterns that represent behavior for intrusion detection. We describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of behavior. To detect anomalous behavior, we generate fuzzy association rules from new audit data and compute the similarity with sets mined from normal data. If the similarity values are below a threshold value, an alarm is issued. We describe an algorithm for computing fuzzy association rules based on Borgelt's (2001) prefix trees, modifications to the computation of support and confidence of fuzzy rules, a new method for computing the similarity of two fuzzy rule sets, and feature selection and optimization with genetic algorithms. Experimental results demonstrate that we can achieve better running time and accuracy with these modifications.

This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.

This paper presents a multiobjective genetic algorithm which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The evolutionary algorithm follows a multiobjective approach in order to optimize in a suitable way the different quality measures used in this kind of problems. Experimental evaluation of the algorithm, applying it to a market problem studied in the University of Mondragón (Spain), shows the validity of the proposal. The application of the proposal to this problem allows us to obtain novel and valuable knowledge for the experts.

Association rules are frequently used for describing association (i.e., co-occurrence) among attribute values in the field of data mining. When an attribute is continuous (i.e., real-valued) such as height, length and weight, its domain is usually discretized into several intervals. Fuzzy rules are recognized as a convenient tool for handling continuous attributes in a human understandable manner. When we use fuzzy rules, the domain of each continuous attribute is discretized into several fuzzy sets. A linguistic label is usually associated with each fuzzy set especially when linguistic interpretations of fuzzy rules are required. In this paper, we first fuzzify the concept of association rules. That is, we show fuzzy versions of two measures (i.e., confidence and support) that are used for evaluating each association rule in the field of data mining. Then we illustrate these two measures of fuzzy rules for function approximation and pattern classification problems. Finally we examine the relation between the classification performance of fuzzy rules and the definition of their certainty grades through computer simulations. Simulation results show that the direct use of confidence as a certainty grade is not always appropriate from the viewpoint of classification performance.

This paper presents Cellular Fuzzy Oriented Classifier Evolution (CFORCE), a generic method for constructing fuzzy rules to divide an image into two segments: object and background. In CFORCE, a pair of fuzzy classification rule sets for object and background is defined as a processing unit, and the identical units are allocated in each pixel of an input image. Each unit computes matching degree of each pixel with object and background class iteratively with considering the matching degree of neighbor units. The algorithm has mainly two features: 1) designing the fuzzy rules using Fuzzy Oriented Classifier Evolution (FORCE) which develops fuzzy rules represented as directed graphs flexibly and automatically by Genetic Algorithm, and 2) performing iterative segmentation with considering spatial relationship between pixels besides local features. In natural image segmentation, many pixels are overlapped between different clusters. Therefore, considering the spatial relationship is important to classify the overlapped pixels correctly. We applied CFORCE to three different object segmentation, and showed that CFORCE extracted object regions successfully.

Neuro-fuzzy systems have been proved effective in training classifiers, especially when it comes to noisy, inaccurate or incomplete datasets. For this reason, and due to their simple comprehensible nature, these systems have become popular in designing classifiers. One of the major challenges in designing a neuro-fuzzy classifier is achieving the optimum system parameters such as the type and position of the membership function as well as its training method. These factors could affect the function of the classifier significantly. In this paper, a novel method based on evolutionary algorithms such as inclined planes optimization algorithm (IPO), particle swarm optimizer (PSO) and genetic algorithm (GA) is introduced to design a neuro-fuzzy classifier in such a way that the accuracy is increased and the error rate is minimized. To prove the efficiency of the proposed method, several experiments are conducted on well-known datasets with different number of classes and different feature vector lengths. Results indicate that the proposed evolutionary-based neuro-fuzzy classifier is superior to a normal neuro-fuzzy classifier in terms of accuracy. In addition, experiments showed that the proposed method is able to properly classify the data with a relatively high stability.

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

Fuzzy rules generation is known an important task in designing fuzzy systems. This article applies an evolutionary fuzzy rules learning scheme to approach desired fuzzy systems having a lower fuzzy rules. The proposed learning scheme overcomes limitations of conventional fuzzy rules generation and completes the complex searching problems to extract the desired fuzzy system. In this article, aggregations of hyper-ellipsoids fuzzy partitions with different sizes and different positions are suggested to approximate the knowledge rule base of fuzzy systems whose membership functions are arbitrarily shaped and flexibly tuned in parameters searching space. Several corresponding parameters in defining the region of such hyper-ellipsoids type membership functions are efficiently selected based on the simple rule extracting technology. Furthermore, the constructed fuzzy system with only two fuzzy rules can be automatically extracted by the evolutional genetic algorithms (GAs) learning scheme with the guide of special fitness function. Finally, both inverted pendulum balance and nonlinear modeling problems are used to illustrate the effectiveness of the proposed method.

In this paper, we introduce new fuzzy-neural networks – Fuzzy Set – based Polynomial Neural Networks (FSPNN) with a new fuzzy set-based polynomial neuron (FSPN) whose fuzzy rules include the information granules obtained through Information Granulation. We investigate the proposed networks from two different aspects to improve the performance of the fuzzy-neural networks. First, We have developed genetic optimization using Genetic Algorithms to find the optimal structure for fuzzy-neural networks. Second, we have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules. The performance of genetically optimized FSPNN (gFSPNN) with fuzzy set-based neural neuron (FSPN) involving information granules is quantified through experimentation.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

To improve the control precision of the closed-loop speed regulation of the brushless DC motor (Brushless DC motor, BLDCM) used in hybrid vehicles, the paper established a fuzzy controller for closed loop control of speed, taking the speed difference and the conversion rate of the speed difference as the inputs, and the increment of the supply voltage as the output, and proposed a method of optimizing fuzzy control rules based on genetic algorithm. Established the mathematical model of BLDCM in MATLAB/Simulink, simulated the traditional PID controller, the fuzzy controller and the fuzzy controller optimized by genetic algorithm. Then the hardware-in-the-loop motor control system is constructed based on dSPACE. The simulation and experiment prove that the optimization of fuzzy rules by genetic algorithm is an effective method to improve the accuracy of speed control, and it has good tracking performance for frequently changing speed requirements in hybrid vehicle.