• Title/Summary/Keyword: Fuzzy KNN

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Optimized KNN/IFCM Algorithm for Efficient Indoor Location (효율적인 실내 측위를 위한 최적화된 KNN/IFCM 알고리즘)

  • Lee, Jang-Jae;Song, Lick-Ho;Kim, Jong-Hwa;Lee, Seong-Ro
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.2
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    • pp.125-133
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    • 2011
  • For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

KNN/PFCM Hybrid Algorithm for Indoor Location Determination in WLAN (WLAN 실내 측위 결정을 위한 KNN/PFCM Hybrid 알고리즘)

  • Lee, Jang-Jae;Jung, Min-A;Lee, Seong-Ro
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.47 no.6
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    • pp.146-153
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    • 2010
  • For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

A Design of Fuzzy Classifier with Hierarchical Structure (계층적 구조를 가진 퍼지 패턴 분류기 설계)

  • Ahn, Tae-Chon;Roh, Seok-Beom;Kim, Yong Soo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.4
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    • pp.355-359
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    • 2014
  • In this paper, we proposed the new fuzzy pattern classifier which combines several fuzzy models with simple consequent parts hierarchically. The basic component of the proposed fuzzy pattern classifier with hierarchical structure is a fuzzy model with simple consequent part so that the complexity of the proposed fuzzy pattern classifier is not high. In order to analyze and divide the input space, we use Fuzzy C-Means clustering algorithm. In addition, we exploit Conditional Fuzzy C-Means clustering algorithm to analyze the sub space which is divided by Fuzzy C-Means clustering algorithm. At each clustered region, we apply a fuzzy model with simple consequent part and build the fuzzy pattern classifier with hierarchical structure. Because of the hierarchical structure of the proposed pattern classifier, the data distribution of the input space can be analyzed in the macroscopic point of view and the microscopic point of view. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

Design of Radial Basis Function with the Aid of Fuzzy KNN and Conditional FCM (퍼지 kNN과 Conditional FCM을 이용한 퍼지 RBF의 설계)

  • Roh, Seok-Beon;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.6
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    • pp.1223-1229
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    • 2009
  • The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information.

Fuzzy Cluster Based Diagnosis System for Classifying Computer Viruses (컴퓨터 바이러스 분류를 위한 퍼지 클러스터 기반 진단시스템)

  • Rhee, Hyun-Sook
    • The KIPS Transactions:PartB
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    • v.14B no.1 s.111
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    • pp.59-64
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    • 2007
  • In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.

Text-independent Speaker Identification Using Soft Bag-of-Words Feature Representation

  • Jiang, Shuangshuang;Frigui, Hichem;Calhoun, Aaron W.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.4
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    • pp.240-248
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    • 2014
  • We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

Face Recognition using Fisherface Method with Fuzzy Membership Degree (퍼지 소속도를 갖는 Fisherface 방법을 이용한 얼굴인식)

  • 곽근창;고현주;전명근
    • Journal of KIISE:Software and Applications
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    • v.31 no.6
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    • pp.784-791
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    • 2004
  • In this study, we deal with face recognition using fuzzy-based Fisherface method. The well-known Fisherface method is more insensitive to large variation in light direction, face pose, and facial expression than Principal Component Analysis method. Usually, the various methods of face recognition including Fisherface method give equal importance in determining the face to be recognized, regardless of typicalness. The main point here is that the proposed method assigns a feature vector transformed by PCA to fuzzy membership rather than assigning the vector to particular class. In this method, fuzzy membership degrees are obtained from FKNN(Fuzzy K-Nearest Neighbor) initialization. Experimental results show better recognition performance than other methods for ORL and Yale face databases.

Interpolation of Color Image Scales (칼라 이미지 스케일의 보간)

  • Kim, Sung-Hwan;Jeong, Sung-Hwan;Lee, Joon-Whoan
    • Science of Emotion and Sensibility
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    • v.10 no.3
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    • pp.289-297
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    • 2007
  • Color image scale captures the knowledge of colorists and represents both adjectives and colors in the same adjective image scales in order to select color(s) corresponding to an adjective. Due to the difficulty of psychological experiment and statistical analysis, in general, only a limited number of colors are located in the color image scales. This can make color selection process hard especially to non-expert. In this paper, we propose an interpolation of color image scale based on the fuzzy K-nearest neighbor method, which provides continuous colors according to the coordinates of the image scales. The experimental results show that the interpolated image scales can be practically useful for color selection process.

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Design and Implementation of Speech Music Discrimination System per Block Unit on FM Radio Broadcast (FM 방송 중 블록 단위 음성 음악 판별 시스템의 설계 및 구현)

  • Jang, Hyeon-Jong;Eom, Jeong-Gwon;Im, Jun-Sik
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2007.11a
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    • pp.25-28
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    • 2007
  • 본 논문은 FM 라디오 방송의 오디오 신호를 블록 단위로 음성 음악을 판별하는 시스템을 제안하는 논문이다. 본 논문에서는 음성 음악 판별 시스템을 구축하기 위해 다양한 특정 파라미터와 분류 알고리즘을 제안 한다. 특정 파라미터는 신호처리 분야(Centroid, Rolloff, Flux, ZCR, Low Energy), 음성 인식 분야(LPC, MFCC), 음악 분석 분야(MPitch, Beat)에서 각각 사용되는 파라미터를 사용하였으며 분류 알고리즘으로는 패턴인식 분야(GMM, KNN, BP)와 퍼지 신경망(ANFIS)을 사용하였고, 거리 구현은 Mahalanobis 거리를 사용하였다.

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