Fuzzy measure and integral are widely used in Multiple Classifier System (MCS). But the number of coefficients involved in the fuzzy integral model grows exponentially with the number of classifiers to be aggregated. ...
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Fuzzy measure and integral are widely used in Multiple Classifier System (MCS). But the number of coefficients involved in the fuzzy integral model grows exponentially with the number of classifiers to be aggregated. The main difficulty is to identify all these coefficients. This paper does an attempt Using 2-additrve fuzzy measure in Multiple Classifier System. Our conclusion is that when different interactions exist in different classifiers the complexity of the computation can be significantly reduced by 2-order additive measure.A simple example is included to illustrate the 2-order additive measure.
Classification is necessary and basic to scientific research. The Chinese loanword has always been a hot spot of studies on Chinese linguistics, however the range of it remained unsettled. The paper will introduce fuz...
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Classification is necessary and basic to scientific research. The Chinese loanword has always been a hot spot of studies on Chinese linguistics, however the range of it remained unsettled. The paper will introduce fuzzy set technology into the discriminant process of Chinese loanwords to compose a reliable and efficient classifier. Simulations verify the efficiency and feasibility.
This paper is to discuss the reduction of computation complexity in decision tree generation for the numerical-valued attributes. The proposed method is based on the partition impurity. The partition impurity minimiza...
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This paper is to discuss the reduction of computation complexity in decision tree generation for the numerical-valued attributes. The proposed method is based on the partition impurity. The partition impurity minimization is used to select the expanded attribute for generation the sub-node during the tree growth. After inducing the unstable cut-points of numerical-attributes, it is analytically proved that the partition impurity minimization can always be obtained at the unstable cut-points. It implies that the computation on stable cut-points may not be considered during the tree growth. Since the stable cut-points are far more than unstable cut-points, the experimental results show that the proposed method can reduce the computational complexity greatly.
A new method, multi-class fuzzy support vector machine of dismissing margin (DMFSVM) based on class-center, is proposed aiming at the outliers and noises which appear in the large quantity samples. Compared with tradi...
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A new method, multi-class fuzzy support vector machine of dismissing margin (DMFSVM) based on class-center, is proposed aiming at the outliers and noises which appear in the large quantity samples. Compared with traditional SVM, this new method eliminates the sensibility of optimal separating hyperplane. It weeds out some sample points which may not be support vectors. And this will be able to decrease the corresponding optimization problem dimension, reduce the memory and the amount of computation, but increase the training speed. At the same time, the new algorithm adopts fuzzy membership function of decreasing Semi-Cauchy type. The advantages are through regulating parameters of fuzzy factor suitably according to the specific circumstances to make the fuzzy factor of isolated points smaller and the fuzzy factor of support vectors larger relatively. So this method can fit the characteristics of fuzzy classification well.
Feature selection is an essential technique used in data mining and machinelearning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is ra...
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Feature selection is an essential technique used in data mining and machinelearning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.
In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta s...
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In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta search. It is helpful to reduce the size of game tree. In this paper, we improved the prototypical one-ply iterative deepening (OPID) and proposed two-ply iterative deepening (TPID). In game tree searching, we extend the search by two plies from the previous iteration. An iterated series of 2-ply, 4-ply, 6-ply,…searches is carried out. In the experiments, we validate that TPID is feasible and effective. Through applying TPID to minimax search and alpha-beta search respectively, we found that the total number of nodes generated in TPID minimax search and TPID alpha-beta search are all reduced compared with OPID.
Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identi...
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Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called CDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.
Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will ov...
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Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of...
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ISBN:
(纸本)9780769538877
Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.
Deep Web can provide us a great amount of high quality information. In order to make full use of the information, it is becoming urgent to establish Deep Web data integration system, in which Deep Web interface integr...
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