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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.
Measurement of hydrophobicity is not only a main way to evaluate the performance of insulators, but also an important factor to ensure the safety of insulators.A novel hydrophobic measurement algorithm based on information fusion technology is proposed in this paper. By the triangle module operator, the membership function of area ratio and shape factor are fused together to classify the hydrophobicity...
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...
In this paper, we consider a fundamental theoretical question: why cannot regular fuzzy implication operator meeting logical properties construct fuzzy system? Through the appropriate choice of the aggregation operator, the center of gravity fuzzy system (COGFS) based on regular fuzzy implication is studied in detail in the paper. Numerical characteristics of the random vector corresponding to the...
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...
This paper presents a method to construct efficient and distinctive descriptors for local image features based on Scale Invariant Features Transform (SIFT), namely, Kernel Independent Component Analysis Scale Invariant Features Transform (KICA-SIFT). KICA-SIFT is a improved version of the conventional SIFT for the two reasons: first, the improved SIFT descriptors are relative invariant to affine transformation,...
The fuzzy time series is introduced by Song and Chissom to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect...
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...
Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately...
Skin color image segmentation is an important part in skin image analysis. Segmentation feature parameter and segmentation arithmetic significantly influence the segmentation result. In this article, we compared RGB, HSV, and Lab color spaces and found that HSV color space as segmentation feature parameter has the advantage. Furthermore, we used an improved Fuzzy-c-means arithmetic (IFCM) in skin...
Aiming at industrial processes with large time-delay, this paper presents a filter time constant λ self-adjusting internal model control (IMC) based on variable domain fuzzy control. A modified variable domain fuzzy control method is proposed on the basis of quantitative factor. Comparing with traditional fuzzy control, simulation results show that the method proposed can improve dynamic quality and...
Although GPS-based travel survey has been studied by many, automated travel mode detection still remains a technical challenge. This paper proposed and tested a fuzzy approach to travel mode recognition from the GPS travel data collected from 32 volunteers for 142 days in Shanghai. Four speed-related fuzzy variables were selected to characterize five movement patterns (walk, bike, bus, rail, and rest)...
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...
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...
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...
Feature selection for text classification is a well-studied problem and the goals are improving classification effectiveness, computational efficiency, or both. In this paper, we propose a two-stage feature selection algorithm based on a kind of feature selection method and latent semantic indexing. Traditional word-matching based text categorization system uses vector space model to represent the...
This paper compares the performance of linear and nonlinear kernels of Support Vector Machines (SVM) used for text classification. The study is motivated by the previous viewpoint that linear SVM performs better than nonlinear one, and that, although there are many investigations have proved that SVM performs well in text classification, there is no serious investigation on the comparison between...
Text Categorization (TC) is an important component in many information organization and information management tasks. In many TC applications, the case-base grows at a fast rate and this causes inefficiency in the case retrieval process. Using Case-Base Maintenance learning via the GC (Generalization Capability) algorithm, which can reduce the case number into KNN algorithm, can improve efficiency...
Hybrid classification model is currently an active research area and successfully solves classification problems in credit scoring. Finding effective classificatory models is important. Classification in credit scoring has been regarded as a critical topic, with its related departments collecting huge amounts of data to avoid making the wrong decision. Filter feature selection model is important in...
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