Mining axiomatic fuzzy set association rules for classification problems

In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics.     

Extracting compact and information lossless sets of fuzzy association rules

Applying classical association rule extraction framework on fuzzy datasets leads to an unmanageably highly sized association rule sets. Moreover, the discretization operation leads to information loss and constitutes a hamper towards an efficient exploitation of the mined knowledge. To overcome such a drawback, this paper proposes the extraction and the exploitation of compact and informative generic basis of fuzzy association rules. The presented approach relies on the extension, within the fuzzy context, of the notion of closure and Galois connection, that we introduce in this paper. In order to select without loss of information a generic subset of all fuzzy association rules, we define three fuzzy generic basis from which remaining (redundant) FARs are generated. This generic basis constitutes a compact nucleus of fuzzy association rules, from which it is possible to informatively derive all the remaining rules. In order to ensure a sound and complete derivation process, we introduce an axiomatic system allowing the complete derivation of all the redundant rules. The results obtained from experiments carried out on benchmark datasets are very encouraging. They highlight a very important reduction of the number of the extracted fuzzy association rules without information loss.     

Mining web documents to find additional query terms using fuzzy association rules

In this paper, we present an application of association rules to query refinement. Starting from an initial set of documents retrieved from the web, text transactions are constructed and association rules are extracted. A fuzzy extension of text transactions and association rules is employed, where the presence of the terms (items) in the documents (transactions) is determined with a value between 0 and 1. The obtained rules offer the user additional terms to be added to the query with the purpose of guiding the search and improving the retrieval.     

Optimal fuzzy counterparts of scheduling rules

The optimality of a fuzzy logic alternative to the usual treatment of uncertainties in a scheduling system using probability theory is examined formally. Fuzzy scheduling techniques proposed in the literature either fuzzify directly the existing scheduling rules, or solve mathematical programming problems to determine the optimal schedules. In the former method, the fuzzy optimality for the optimal scheduling rules is usually not justified but still assumed. In this paper, the necessary conditions for fuzzy optimality are defined, and fuzzy counterparts of some of the well-known scheduling rules such as shortest processing time (SPT) and earliest due date (EDD) are developed.     

On the representation of fuzzy rules

In fuzzy logic, connectives have a meaning that, can frequently be known through the use of these connectives in a given context. This implies that there is not a universal-class for each type of connective, and because of that several continuous t-norms, continuous t-conorms and strong negations, are employed to represent, respectively, the and, the or, and the not. The same happens with the case of the connective If/then for which there is a multiplicity of models called T-conditionals or implications. To reinforce that there is not a universal-class for this connective, four very simple classical laws translated into fuzzy logic are studied.     

Redundant fuzzy rules exclusion by genetic algorithms

A genetic-algorithm-based method for exclusion of the potential redundant if-then fuzzy rules that have been extracted from numerical input-output data is proposed. The main idea is the input-space separation into activation rectangles, corresponding to certain output intervals. The generation of fuzzy rules and the membership functions are based on these activation rectangles and appropriate fuzzy rules inference mechanism is proposed. As the method usually produces too many rules, it is necessary to exclude the potential redundant if-then rules. The concept for varying the family of sensitivity parameters, defining the overlapping of the fuzzy regions is proposed. The genetic algorithms are used to resolve the following combinatorial optimization problem: the generation of families of sensitivity parameters. In this way the potential redundant if-then fuzzy rules are excluded.

The method formalizes the synthesis of the fuzzy system and could be used for function approximation, classification and control purposes. An illustrative example for implementation of the method for traffic fuzzy control is given.     

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising.     

Vehicle dispatching system based on Taguchi-tuned fuzzy rules

This paper presents the development of a dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment which is based on a hybrid Fuzzy–Taguchi approach. A fuzzy decision-making system emulates the human behavior necessary for multi-objective directed decision making in a dynamically evolving environment. A statistical approach based on the Taguchi method tunes the fuzzy rules to achieve near optimal performance. Simulation results demonstrate the effectiveness of this marriage of computational tools in dealing with the well-known NP-complete scheduling problem.     

Interpretation of fuzzy data by means of fuzzy rules with applications to speech recognition

Recently, a speech recognition methodology has been proposed which has as one of its main principles the explicit assumption of intrinsic uncertainty of the data (speech signals) and inexactness of the knowledge (acoustic phonetic, etc…) available to interpret them. The main problem presented by this methodology is that of parsing ‘fuzzy data’ by means of ‘fuzzy rules’. To solve this problem, an appropriate fuzzy parsing and interpretation scheme has been proposed. It assumes the data to be represented as strings of ‘fuzzy symbols’, defined as fuzzy sets over the appropriate set of categories, and knowledge as finite-state networks with the arcs labelled by fuzzy symbols of the same type. A formal presentation of this scheme is the main topic of this paper. Included is a brief discussion of the application to Automatic Speech Recognition, and a summary of some results obtained from an implementation example.     

The chain and substitution rules of interval-valued intuitionistic fuzzy calculus

The interval-valued intuitionistic fuzzy set proposed by Atanassov is the extension of intuitionistic fuzzy set. It extends the membership degree and non-membership to interval values instead of a single value. So it contains more possible values and maybe more considerate. Among all the researches, the exploration on the calculus of interval-valued intuitionistic fuzzy set is entirely new. Recently, Zhao et al. (Int J Comput Intell Syst 9:36–56, 2016) proposed the concept of interval-valued intuitionistic fuzzy function (IVIFF) and gave a calculation method of derivative and differential of IVIFF. Based on this work, in this paper, firstly, we utilize a new and easier method to express the derivative and differential of IVIFF. Secondly, we propose the chain rules of derivative and the form invariance of differential in the interval-valued intuitionistic fuzzy environment. In addition, some properties of the substation rules for interval-valued intuitionistic fuzzy indefinite integrals and definite integrals are also developed.     

A learning process for fuzzy control rules using genetic algorithms

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the thrid one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable genetic algorithms.     

Combining boosting and evolutionary algorithms for learning of fuzzy classification rules

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances that are classified correctly by the new rule. Therefore, the next rule generation cycle focuses on fuzzy rules that account for the currently uncovered or misclassified instances. The weight of a fuzzy rule reflects the relative strength the boosting algorithm assigns to the rule class when it aggregates the casted votes. The approach is compared with other classification algorithms for a number problem sets from the UCI repository.     

Selecting optimum maintenance strategy by fuzzy interactive linear assignment method

In the current competitive environment managers of manufacturing and service organizations try to make their organizations competitive by providing timely delivery of high quality products. Maintenance, as a system, plays a key role in reducing cost, minimizing equipment downtime, improving quality, increasing productivity and providing reliable equipment and as a result achieving organizational goals and objectives. This paper presents a new approach for selecting optimum maintenance strategy using qualitative and quantitative data through interaction with the maintenance experts. This approach has been based on linear assignment method (LAM) with some modifications to develop interactive fuzzy linear assignment method (IFLAM).     

Financial planning for the preventive maintenance of power distribution systems via fuzzy AHP

One of the most important objectives of electricity distribution companies is to improve the reliability of the distribution networks. To this end, the electricity distribution companies try to optimally use the existing financial resources in the planning of preventive maintenance (PM) programs to reduce the imposed costs on the system due to the failure of network components and to improve the network reliability. In fuzzy analytical hierarchical process (fuzzy AHP) method, the degree of network reliability and the effectiveness of PM budget in the improvement of network reliability are selected as decision criteria in the budget allocation procedure. The areas served by the power distribution network are prioritized relative to each other and are assigned weights based on these priorities. The PM budget is determined based on the obtained weights. The medium voltage distribution network of seven areas in the city of Tehran have been selected for the implementation of the proposed method and the analysis of the obtained results. © 2014 Wiley Periodicals, Inc. Complexity 21: 36–46, 2016     

Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering,GA and unscented filter

This paper presents a two stage procedure for building optimal fuzzy model from data for nonlinear dynamical systems. Both stages are embedded into Genetic Algorithm (GA) and in the first stage emphasis is placed on structural optimization by assigning a suitable fitness to each individual member of population in a canonical GA. These individuals represent coded information about the structure of the model (number of antecedents and rules). This information is consequently utilized by subtractive clustering to partition the input space and construct a compact fuzzy rule base. In the second stage, Unscented Filter (UF) is employed for optimization of model parameters, that is, parameters of the input–output Membership Functions (MFs).     

Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods

In power distribution systems, with their great vastness and various outage causes, one of the most important problems of power distribution companies is to select a suitable maintenance strategy of system elements and method of financial planning for the maintenance of system elements with the two objectives of decrease in outage costs and improvement of system reliability. In this article, a practical method is introduced for the selection of a suitable system elements maintenance strategy; moreover, to plan the preventive maintenance budget for the system elements, two methods are offered: the cost optimization method and the fuzzy Analytic Hierarchy Process (AHP) method. In the former method, a new model of system maintenance cost is offered. This model, based on system outage information, the elements maintenance costs are determined as functions of system reliability indices and preventive maintenance budget. The latter method, too, a new guideline is introduced for considering the cost and reliability criteria in the trend of preventive maintenance budget planning. In this method, the preventive maintenance budget for the elements is determined based on relative priority of elements with reliability criteria. © 2015 Wiley Periodicals, Inc. Complexity 21: 70–88, 2016     

Incremental Evaluation of Risky Choices

This paper compares the incremental method of valuation and its possible effects on choices analysed through utility theory with similar zero-base valuations. Analysis shows how biases can arise using incremental valuations in situations with decreasing risk aversion. This could be important in certain circumstances, e.g. delegated decision-making in cases where the total value of an organisation is unknown to the decision-maker or difficult to quantify.