• Title/Summary/Keyword: Nearest Neighborhood classifier

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Design of Nearest Prototype Classifier by using Differential Evolutionary Algorithm (차분진화 알고리즘을 이용한 Nearest Prototype Classifier 설계)

  • Roh, Seok-Beom;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.4
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    • pp.487-492
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    • 2011
  • In this paper, we proposed a new design methodology to improve the classification performance of the Nearest Prototype Classifier which is one of the simplest classification algorithm. To optimize the position vectors of the prototypes in the nearest prototype classifier, we use the differential evolutionary algorithm. The optimized position vectors of the prototypes result in the improvement of the classification performance. The new method to determine the class labels of the prototypes, which are defined by the differential evolutionary algorithm, is proposed. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

Optimal k-Nearest Neighborhood Classifier Using Genetic Algorithm (유전알고리즘을 이용한 최적 k-최근접이웃 분류기)

  • Park, Chong-Sun;Huh, Kyun
    • Communications for Statistical Applications and Methods
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    • v.17 no.1
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    • pp.17-27
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    • 2010
  • Feature selection and feature weighting are useful techniques for improving the classification accuracy of k-Nearest Neighbor (k-NN) classifier. The main propose of feature selection and feature weighting is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. In this paper, a novel hybrid approach is proposed for simultaneous feature selection, feature weighting and choice of k in k-NN classifier based on Genetic Algorithm. The results have indicated that the proposed algorithm is quite comparable with and superior to existing classifiers with or without feature selection and feature weighting capability.

Hand Gesture Interface Using Mobile Camera Devices (모바일 카메라 기기를 이용한 손 제스처 인터페이스)

  • Lee, Chan-Su;Chun, Sung-Yong;Sohn, Myoung-Gyu;Lee, Sang-Heon
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.5
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    • pp.621-625
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    • 2010
  • This paper presents a hand motion tracking method for hand gesture interface using a camera in mobile devices such as a smart phone and PDA. When a camera moves according to the hand gesture of the user, global optical flows are generated. Therefore, robust hand movement estimation is possible by considering dominant optical flow based on histogram analysis of the motion direction. A continuous hand gesture is segmented into unit gestures by motion state estimation using motion phase, which is determined by velocity and acceleration of the estimated hand motion. Feature vectors are extracted during movement states and hand gestures are recognized at the end state of each gesture. Support vector machine (SVM), k-nearest neighborhood classifier, and normal Bayes classifier are used for classification. SVM shows 82% recognition rate for 14 hand gestures.

Preference Prediction System using Similarity Weight granted Bayesian estimated value and Associative User Clustering (베이지안 추정치가 부여된 유사도 가중치와 연관 사용자 군집을 이용한 선호도 예측 시스템)

  • 정경용;최성용;임기욱;이정현
    • Journal of KIISE:Software and Applications
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    • v.30 no.3_4
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    • pp.316-325
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    • 2003
  • A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.