共查询到8条相似文献,搜索用时 15 毫秒
1.
Parallel distributed genetic fuzzy rule selection 总被引:1,自引:1,他引:0
Yusuke Nojima Hisao Ishibuchi Isao Kuwajima 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(5):511-519
Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rule-based classifiers. It is,
however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple
but effective idea to improve the scalability of genetic fuzzy rule selection to large data sets. Our idea is based on its
parallel distributed implementation. Both a training data set and a population are divided into subgroups (i.e., into training
data subsets and sub-populations, respectively) for the use of multiple processors. We compare seven variants of the parallel
distributed implementation with the original non-parallel algorithm through computational experiments on some benchmark data
sets. 相似文献
2.
当模糊规则库是稀疏型时,利用Kóczy线性插值推理方法不能保证推理结论的正规性和凸性,为了解决这一问题,石岩曾提出了插值推理方法的推理条件,当满足这些条件时利用Kóczy线性插值推理方法得到的推理结论也满足正规性和凸性;但是这些条件却限制了模糊推理系统的应用,而且如果多次推理中在同一输入点遇到稀疏情况,必须进行相同的计算才能得到正确的推理结果,这样增加了系统的计算量,降低了系统的速度和效率.因此提出了一种新的稀疏模糊推理方法,不仅能够简单的给出正确的推理结果,还能在相应的位置增加规则,提高规则库的紧密程度. 相似文献
3.
V. Ravi H.-J. Zimmermann 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2001,5(2):152-159
This paper presents a novel hybrid of the two complimentary technologies of soft computing viz. neural networks and fuzzy
logic to design a fuzzy rule based pattern classifier for problems with higher dimensional feature spaces. The neural network
component of the hybrid, which acts as a pre-processor, is designed to take care of the all-important issue of feature selection.
To circumvent the disadvantages of the popular back propagation algorithm to train the neural network, a meta-heuristic viz.
threshold accepting (TA) has been used instead. Then, a fuzzy rule based classifier takes over the classification task with
a reduced feature set. A combinatorial optimisation problem is formulated to minimise the number of rules in the classifier
while guaranteeing high classification power. A modified threshold accepting algorithm proposed elsewhere by the authors (Ravi
V, Zimmermann H.-J. (2000) Eur J Oper Res 123: 16–28) has been employed to solve this optimization problem. The proposed methodology
has been demonstrated for (1) the wine classification problem having 13 features and (2) the Wisconsin breast cancer determination
problem having 9 features. On the basis of these examples the results seem to be very interesting, as there is no reduction
in the classification power in either of the problems, despite the fact that some of the original features have been completely
eliminated from the study. On the contrary, the chosen features in both the problems yielded 100% classification power in
some cases. 相似文献
4.
Arijit Laha 《Advanced Engineering Informatics》2007,21(3):281-291
Credit-risk evaluation is a very challenging and important problem in the domain of financial analysis. Many classification methods have been proposed in the literature to tackle this problem. Statistical and neural network based approaches are among the most popular paradigms. However, most of these methods produce so-called “hard” classifiers, those generate decisions without any accompanying confidence measure. In contrast, “soft” classifiers, such as those designed using fuzzy set theoretic approach; produce a measure of support for the decision (and also alternative decisions) that provides the analyst with greater insight. In this paper, we propose a method of building credit-scoring models using fuzzy rule based classifiers. First, the rule base is learned from the training data using a SOM based method. Then the fuzzy k-nn rule is incorporated with it to design a contextual classifier that integrates the context information from the training set for more robust and qualitatively better classification. Further, a method of seamlessly integrating business constraints into the model is also demonstrated. 相似文献
5.
Xiaoxia Huang 《Information Sciences》2007,177(23):5404-5414
The aim of this paper is to solve the portfolio selection problem when security returns contain both randomness and fuzziness. Utilizing a different perspective, this paper gives a new definition of risk for random fuzzy portfolio selection. A new optimal portfolio selection model is proposed based on this new definition of risk. A new hybrid intelligent algorithm is designed for solving the new optimization problem. In the proposed new algorithm, neural networks are employed to calculate the expected value and the chance value. These greatly reduce the computational work and speed up the process of solution as compared with the random fuzzy simulation used in our previous algorithm. A numerical example is also presented to illustrate the new modelling idea and the proposed new algorithm. 相似文献
6.
Chi-Ho Tsang Author Vitae Author Vitae Hanli Wang Author Vitae 《Pattern recognition》2007,40(9):2373-2391
Classification of intrusion attacks and normal network traffic is a challenging and critical problem in pattern recognition and network security. In this paper, we present a novel intrusion detection approach to extract both accurate and interpretable fuzzy IF-THEN rules from network traffic data for classification. The proposed fuzzy rule-based system is evolved from an agent-based evolutionary framework and multi-objective optimization. In addition, the proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction. To evaluate the classification and feature selection performance of our approach, it is compared with some well-known classifiers as well as feature selection filters and wrappers. The extensive experimental results on the KDD-Cup99 intrusion detection benchmark data set demonstrate that the proposed approach produces interpretable fuzzy systems, and outperforms other classifiers and wrappers by providing the highest detection accuracy for intrusion attacks and low false alarm rate for normal network traffic with minimized number of features. 相似文献
7.
Chao-Chun Liu Author Vitae Dao-Qing Dai Author Vitae Hong Yan Author Vitae 《Pattern recognition》2010,43(8):2993-3002
Microarray data are used in many biomedical experiments. They often contain missing values which significantly affect statistical algorithms. Although a number of imputation algorithms have been proposed, they have various limitations to exploit local and global information effectively for estimation. It is necessary to develop more effective techniques to solve the data imputation problem. In this paper, we propose a theoretic framework of local weighted approximation for missing value estimation, based on the Taylor series approximation. Besides revealing that k-nearest neighbor imputation (KNNimpute) is a special case of the framework, we focus on the study of its linear case—local weighted linear approximation imputation (LWLAimpute) from theory to experiment. Experimental results show that LWLAimpute and its iterative version can achieve better performance than some existing imputation methods, the superiority becomes more significant with increasing level of missing values. 相似文献
8.
A. Chatterjee A. Rakshit P. Siarry 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(8):631-642
The present paper is a humble attempt to develop a fuzzy function approximator which can completely self-generate its fuzzy
rule base and input-output membership functions from an input-output data set. The fuzzy system can be further adapted to
modify its rule base and output membership functions to provide satisfactory performance. This proposed scheme, called generalised
influential rule search scheme, has been successfully implemented to develop pure fuzzy function approximators as well as
fuzzy logic controllers. The satisfactory performance of the proposed scheme is amply demonstrated by implementing it to develop
different major components in a process control loop. The versatility of the algorithm is further proved by implementing it
for a benchmark nonlinear function approximation problem. 相似文献