Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all poss...
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ISBN:
(纸本)9781424420957
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.
The Fuzzy Support Vector machines (FSVMs) can be used to deal with multiclass classification problems where the key issue is to solve a quadratic programming problem. This paper introduces a new Fuzzy Multiclass Suppo...
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ISBN:
(纸本)9780769533049
The Fuzzy Support Vector machines (FSVMs) can be used to deal with multiclass classification problems where the key issue is to solve a quadratic programming problem. This paper introduces a new Fuzzy Multiclass Support Vector machines (FMSVMs) based on compact description of data, which extends the exiting support vector machine method to the case of k-class problem in one optimization task (quadratic programming) by considering the relative location of samples to the origin and the knowledge of ambiguity associated with the membership of samples for a given class. The fuzzy membership is defined by not only the relation between a sample and its cluster center, but also by the affinity among samples. Compared with the existing SVMs, our new proposed FMSVMs have the improvement in aspects of classification accuracy and reducing the effects of noises and outliers.
In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzz...
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In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzzy sets. Then, some properties on compositions of fuzzy relations is discussed. We derived that the distributive laws under union and composition of type-2 fuzzy relations is valid. An example shows the failure of distributive laws under intersection and composition.
A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a...
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A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a linear programming model. For the linear separable classification problem, if an instance is regarded as a point of the instances space, the overlap does not still occur between the convex hulls of different classes after a feature is deleted, then we can delete that feature. Repeat this process, an algorithm for feature selection is given. Experimental results show the effectiveness of the algorithm.
MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect th...
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MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.
The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phra...
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The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phrase and keywordchanges the feature extraction of Chinese text fromsyntax and semantic *** the first,analyzing thefeature of baseNP and basedVP,and then make somewords into baseNP and baseVP which accord to therules of phrase,give WSD to other words in the *** paper proposes a stepwise feature extraction fromword to *** experiment results show that thismethod can perform much better than traditionalfeature extraction method it can improve the textclassification precision and recall.
This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and...
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This paper presents an approach to instance selection for the nearest neighbor rule which aims to obtain a condensed set with high condensing rate and prediction accuracy. By making an improvement on MCS algorithm and allowing certain error rate on the training set, a condensed set with high condensing rate and satisfying prediction accuracy is obtained. The condensed set is order-independent of the training instances and insensitive to noise. Comparative experiments have been conducted on real data sets, and the results show its superiority to MCS and FCNN in terms of condensing rate and prediction accuracy.
There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-ma...
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There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-making. In this paper, based on the importance of the fuzzy condition attributes, a new method generating a fuzzy decision tree is proposed, which uses the important degree of the fuzzy condition attribute with respect to the fuzzy decision attributes to select attributes to expand the branches of a fuzzy decision tree. A comparison between the new method and fuzzy ID3 is provided. It is shown that the new method is more efficient than fuzzy ID3.
This paper proposes an image recognition method, which consists of two steps: features extraction based on wavelet transform and image recognition using artificial neural networks. More specifically, wavelet transform...
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This paper proposes an image recognition method, which consists of two steps: features extraction based on wavelet transform and image recognition using artificial neural networks. More specifically, wavelet transform is used to decompose the original image into different frequency sub-bands, then a set of features are extracted from the wavelet coefficients. The feature set as input fed into neural network for recognition. The experimental results confirmed effectiveness of the proposed approach.
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all poss...
详细信息
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.
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