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A fuzzy decision tree can be constructed from a training set of cases and converted into a set of fuzzy rules. In this paper, the reasoning ability of four inductive operators, which are used for applying fuzzy rules to classification, are analyzed and compared. The purpose of this study is to show some useful guidelines on how to choose an appropriate operator for classified problem.
This paper is concerned with the fuzzy support vector classification, in which both of the type of the output training point and the value of the final fuzzy classification function are triangle fuzzy number. First, the fuzzy classification problem is formulated as a fuzzy chance constrained programming. Then, we transform this programming into its equivalence quadratic programming. Final, a fuzzy...
First, we classify the objects in continuous domain decision table according to fuzzy clustering; then, combining rough set theory with fuzzy set theory, an attribute reduct algorithm of decision table with continuous attributes is put forward; at last, a rule extraction algorithm is proposed and also the validity of this algorithm is accounted for through an example.
An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our improved kNN algorithm focuses on finding out the...
Aiming at the knowledge mining from fuzzy and uncertain information, the definition mode and properties of the fuzzy formal context are discussed in the paper. The method of constructing the fuzzy concept lattice of the fuzzy formal context is proposed, in that the definition of fuzzy product concept is the core: the intents of two concepts are combined to form the intent of the product concept; using...
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate...
In machine learning classification, the classifier can be described by some rules, and the rules can be expressed by fuzzy granules corresponding to fuzzy concepts. In this paper we will introduce fuzzy information granulation to the process of building fuzzy classifier. Furthermore, we will present an optimized information granulation based machine learning classification algorithm. Experiments carried...
Eighty eight tobacco samples from six provinces in China, of which the contents of rare earth elements (REEs) were determined by microwave digestion-inductively coupled plasma mass spectrometry method. A fuzzy clustering method, fuzzy c-means (FCM), was used for classification of the different kinds of tobaccos based on their contents of REEs. The results show that FCM clustering analysis is a valid...
Intrusion of network which couldn't be analyzed, detected and prevented may make whole network system paralyze while the abnormally detection can prevent it by detecting the known and unknown character of data. A mixed fuzzy clustering algorithm that uses Quantum-behaved Particle Swarm Optimization (QPSO) algorithm and combines with Fuzzy C-means (FCM) is adopted in this paper and used in abnormally...
This paper compares four commonly used fuzzy analytical methods for remote sensing digital image classification, i.e. fuzzy c-means, semi-supervised fuzzy cluster labeling, fuzzy nearest neighbor, and object-oriented fuzzy classifiers. Merits and weak points of each method were examined through a case study with a multispectral high-resolution airborne digital image of urban settings. Results showed...
In the pattern recognition subspace method, the researcher has paid more attention to extract feature subspace, then expressed individual prototype with the training sample mean. Because the number of training sample is limited, there is certain difference between the sample mean and the individual prototype. In order to reduce this difference, a sample restraint clustering algorithm was proposed,...
This paper proposes a novel vehicle detecting approach for surveillance scenes with single stationary camera. Difference accumulative based background modeling method is used for background modeling. Background subtraction operation is used for detecting moving vehicles and Otsu method is used to threshold the background difference image. Subtractive clustering algorithm is applied for vehicle locating...
A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the...
The advantages of both grey clustering method and fuzzy ISODATA method were analyzed and colligated. First, the decision-making evaluation results of uncertainty system with small sample were acquired with grey clustering method. Then, applying the fuzzy ISODATA model to learn and revise the results of above grey clustering, an optimal fuzzy classification could be produced through iterative operation...
Supervised classification in remote sensing imagery is receiving increasing attention in current research. In order to improve the classification ability, a lot of spatial-features have been utilized. Unfortunately, too many features often cause classifier over-fit to a certain features' character and lead to lower classification accuracy. Feature selection algorithms have utilized to select useful...
Learning from imbalanced data sets presents a new challenge to machine learning community, as traditional methods are biased to majority classes and produce poor detection rate of minority classes. This paper presents a new approach, namely fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning to improve the classification performance of minority class. The approach defines fuzzy...
This study utilizes a fuzzy message requirement classifiers system (FMRCS) that integrates both learning and inference into the learning of the computer troubleshooting ability and adopts a teaching strategy of problem-solving. The main purpose in this study is to guide learners to have the conspicuous direction when they face some computer troubles. Consequently, learners can be based on FMRCS with...
This paper introduces the C-means fuzzy clustering method to evaluate the road traffic status. During the analysis, road traffic status was categorized into four types by using ISODATA algorithm based on expert knowledge. Meanwhile, RBF neural network classification model was established to evaluate the road traffic status. The implementation results showed that the proposed method was capable of...
Aiming to these presented frequent neighboring class set mining algorithms have some repeated computing and redundancy candidate frequent neighboring class set when these algorithms extract frequent neighboring class set, this paper proposes an algorithm of mining frequent neighboring class set based on increasing sequence, which is suitable for mining frequent neighboring class set of objects in...
Hyper surface classification (HSC) based on Jordan Curve Theorem is proven to be a simple and effective method to classify large datasets. Like most of classification algorithms, noise could also impact its accuracy even if the HSC algorithm limits the influence of noise in a local small region. In this paper, we propose a method that intuitively captures the primary goal of improving the accuracy...
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