Journal of Intelligent & Fuzzy Systems - Volume 22, issue 1

Authors: Huang, Yin-Fu | Wu, Chieh-Ming

Article Type: Research Article

Abstract: The subject of this paper is the mining of generalized association rules using pruning techniques. Given a large transaction database and a hierarchical taxonomy tree of the items, we attempt to find the association rules between the items at different levels in the taxonomy tree under the assumption that original frequent itemsets and association rules have already been generated in advance. The primary challenge of designing an efficient mining algorithm is how to make use of the original frequent itemsets and association rules to directly generate new generalized association rules, rather than re-scanning the database. In the proposed algorithms GMAR …(Generalized Mining Association Rules) and GMFI (Generalized Mining Frequent Itemsets), we use join methods and/or pruning techniques to generate new generalized association rules. After several comprehensive experiments, we find that both algorithms are much better than BASIC and Cumulate algorithms, since they generate fewer candidate itemsets, and furthermore the GMAR algorithm prunes a large amount of irrelevant rules based on the minimum confidence. Show more

Keywords: Data mining, generalized association rules, taxonomy trees, frequent itemsets, maximal itemsets, pruning techniques

DOI: 10.3233/IFS-2010-0469

Citation: Journal of Intelligent & Fuzzy Systems, vol. 22, no. 1, pp. 1-13, 2011

Authors: Egrioglu, Erol | Aladag, Cagdas Hakan | Basaran, Murat A. | Yolcu, Ufuk | Uslu, Vedide R.

Article Type: Research Article

Abstract: In fuzzy time series analysis, the determination of the interval length is an important issue. In many researches recently done, the length of intervals has been intuitively determined. In order to efficiently determine the length of intervals, two approaches which are based on the average and the distribution have been proposed by Huarng [4]. In this paper, we propose a new method based on the use of a single variable constrained optimization to determine the length of interval. In order to determine optimum length of interval for the best forecasting accuracy, we used a MATLAB function which is employing an …algorithm based on golden section search and parabolic interpolation. Mean square error is used as a measure of forecasting accuracy so the objective function value is mean square error value for forecasted observations. The proposed method was employed to forecast the enrollments of the University of Alabama to show the considerable outperforming results. Show more

Keywords: Forecasting, fuzzy sets, fuzzy time series, length of interval, optimization

DOI: 10.3233/IFS-2010-0470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 22, no. 1, pp. 15-19, 2011

Authors: Sałat, Robert | Osowski, Stanisław

Article Type: Research Article

Abstract: The paper is concerned with the application of Support Vector Machine (SVM) to the fault location in the analog electrical circuits. The recognition of fault is based on the measurements of the accessible terminal voltage and current of the network at the set of frequencies. The SVM network is applied as the recognizing system and as the classifier. The important feature of the proposed solution is its high accuracy and great speed of operation. Once the network has been trained, the recognition of fault is achieved immediately, irrespective of the size of the circuit. Thus the solution is suited for …real time applications for fault location in electrical circuits. The numerical results of recognition of faulty elements in two different structures of electrical filters are presented and discussed in the paper. Show more

Keywords: Parametric fault recognition in analog circuits, neural network, Support Vector Machine

DOI: 10.3233/IFS-2010-0471

Citation: Journal of Intelligent & Fuzzy Systems, vol. 22, no. 1, pp. 21-31, 2011

Authors: Makrehchi, Masoud | Kamel, Mohamed S.

Article Type: Research Article

Abstract: In this paper, a framework for automatic generation of fuzzy membership functions and fuzzy rules from training data is proposed. The main focus of this paper is designing fuzzy if-then classifiers; however the proposed method can be employed in designing a wide range of fuzzy system applications. After the fuzzy membership functions are modeled by their supports, an optimization technique, based on a multi-objective real coded genetic algorithm with adaptive cross over and mutation probabilities, is implemented to find near optimal supports. Employing interpretability constraint in parameter representation and encoding, we ensure that the generated fuzzy membership function does have …a semantic meaning. The fitness function of the genetic algorithm, which estimates the quality of the generated membership functions, consists of two elements: (i) the Shannon entropy and mutual information measures to measure diversity of the data distribution in a hypercube; and (ii) the number of generated fuzzy rules addressing the measure of compactness of the fuzzy system. Finally membership functions are tuned to yield optimal classifier hypercubes, which represent the predictivity and discriminating power of the classifier. Fuzzy rules of the classifier are derived from the optimal hypercubes. Using the proposed approach to designing fuzzy if-then classifiers, we are also able to evaluate the generated membership functions and compare the results with that of other techniques which have been previously reported in the literature.Using the experimental result, we show that the proposed approach outperforms other techniques in low resolutions. It means that theproposed approach can achieve satisfying result with lower complexity. Show more

DOI: 10.3233/IFS-2010-0472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 22, no. 1, pp. 33-52, 2011