• Title/Summary/Keyword: fuzzy-Bayesian classifier

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Learning Distribution Graphs Using a Neuro-Fuzzy Network for Naive Bayesian Classifier (퍼지신경망을 사용한 네이브 베이지안 분류기의 분산 그래프 학습)

  • Tian, Xue-Wei;Lim, Joon S.
    • Journal of Digital Convergence
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    • v.11 no.11
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    • pp.409-414
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    • 2013
  • Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

An Approach to Combining Classifier with MIMO Fuzzy Model

  • Kim, Do-Wan;Park, Jin-Bae;Lee, Yeon-Woo;Joo, Young-Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.05a
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    • pp.182-185
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    • 2003
  • This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.

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Pruning and Learning Fuzzy Rule-Based Classifier

  • Kim, Do-Wan;Park, Jin-Bae;Joo, Young-Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.663-667
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    • 2004
  • This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

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Human-Livestock Classifier by Using Fuzzy Bayesian Algorithm (퍼지-베이시안을 이용한 인간.가축 분류)

  • Oh, Myung-Jae;Joo, Young-Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.10
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    • pp.1941-1945
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    • 2011
  • In this paper, we propose a real-time classifier to distinguish humans from livestock by using the spatial integral. The image-difference method and the Expectation Maximization are used to reduce noises in input image. A histogram analysis based on Simulated Annealing and the fuzzy-Bayesian algorithm are used to classify human and livestock. Finally, the experiment results show the validity of the proposed method.

A Study on Short-Term Load Forecasting System Using Data Mining (데이터 마이닝을 이용한 단기부하예측 시스템 연구)

  • Kim, Do-Wan;Park, Jin-Bae;Kim, Juhg-Chan;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.588-591
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    • 2003
  • This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

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A Study on the Classification for Satellite Images using Hybrid Method (하이브리드 분류기법을 이용한 위성영상의 분류에 관한 연구)

  • Jeon, Young-Joon;Kim, Jin-Il
    • The KIPS Transactions:PartB
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    • v.11B no.2
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    • pp.159-168
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    • 2004
  • This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

Fuzzy Clustering Model using Principal Components Analysis and Naive Bayesian Classifier (주성분 분석과 나이브 베이지안 분류기를 이용한 퍼지 군집화 모형)

  • Jun, Sung-Hae
    • The KIPS Transactions:PartB
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    • v.11B no.4
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    • pp.485-490
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    • 2004
  • In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

Fingerprinting Bayesian Algorithm for Indoor Location Determination (실내 측위 결정을 위한 Fingerprinting Bayesian 알고리즘)

  • Lee, Jang-Jae;Kwon, Jang-Woo;Jung, Min-A;Lee, Seong-Ro
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.6B
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    • pp.888-894
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    • 2010
  • For the indoor positioning, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor positioning which does not require any special equipments dedicated for positioning. The deployment of a fingerprinting method consists of off-line phase and on-line phase and more efficient and accurate methods have been studied. This paper proposes a bayesian algorithm for wireless fingerprinting and indoor location determination using fuzzy clustering with bayesian learning as a statistical learning theory.

Multi-Modal Biometrics Recognition Method of Face Recognition using Fuzzy-EBGM and Iris Recognition using Fuzzy LDA (Fuzzy-EBGM을 이용한 얼굴인식과 Fuzzy-LDA를 이용한 홍채인식의 다중생체인식 기법 연구)

  • Go Hyoun-Joo;Kwon Mann-Jun;Chun Myung-Ceun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.299-301
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    • 2005
  • 본 연구는 생체정보를 이용하여 개인을 인증하고 확인하기 위한 방법으로 기존 단일 생체인식 기법의 단점을 보완하기 위해 홍채와 얼굴을 이용한 다중생체인식(Multi-Modal Biometrics Recognition)기법을 연구하였다. 중국 홍채 데이터베이스 CASIA(Chinese Academy of Science)에 Gabor Wavelet과 FLDA(Fuzzy Linear Discriminant Analysis)를 사용하여 특징벡터를 획득하였으며, FERET(FERET(Face Recognition Technology) 얼굴영상데이터를 사용하여 FERET 연구에서 매우 우수한 성능을 보인 EBGM알고리듬으로 특징벡터를 획득하였다. 이로부터 얻어진 두 score 값에 대하여 다양한 균등화 과정을 시도해 보았으며, 등록자와 침입자를 구분하기 위한 Fusion Algorithm으로 Bayesian Classifier, Support vector machine, Fisher's linear discriminant를 사용하였다. 또한, 널리 사용되는 방법 중 Weighted Summation을 이용하여 다중생체인식의 성능을 비교해 보았다.

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A Study of Short-Term Load Forecasting System Using Data Mining (데이터 마이닝을 이용한 단기 부하 예측 시스템 연구)

  • Joo, Young-Hoon;Jung, Keun-Ho;Kim, Do-Wan;Park, Jin-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.2
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    • pp.130-135
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    • 2004
  • This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.