IKNN: Informative K-Nearest Neighbor Pattern Classification

Song, Yang; Huang, Jian; Zhou, Ding; Zha, Hongyuan; Giles, C. Lee

doi:10.1007/978-3-540-74976-9_25

Cited by 149 publications

(101 citation statements)

References 11 publications

(22 reference statements)

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“…Despite the simplicity of the model, it employs the strategy of using the distance measure designed by the researcher, thus closely mimicking the human decision making [26]. Moreover, unlike other verifiers, the K-NN does not require a large number of parameters.…”

Section: Verificationmentioning

confidence: 99%

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Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database.

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Section: Verificationmentioning

confidence: 99%

“…Almost all verifiers depend, in one way or the other, on a given distance measure. K-NN is advantageous in that it reflects the human decision making because it is only based on the distance measure designed by the researcher [26]. K-NN also does not involve a lot of parameters like other verifiers.…”

Section: Introductionmentioning

confidence: 99%

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Azmi

Nasien

Omar

2016

Multimed Tools Appl

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“…Se clasifica a la clase del patrón k más cercano, donde k es un entero positivo generalmente impar. Desde los artículos pioneros de este método hasta modificaciones al método original como los presentados en [24], [25], [26], [27], [28] en los cuales se presentan variación del método original o la combinación de clasificadores. Los cuales han demostrado en las diferentes aplicaciones que se ha utilizado este método, que es uno de más eficientes que existen para el reconocimiento y clasificación de patrones.…”

Section: Clasificador K-nnunclassified

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

et al. 2016

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Resumen-Se presenta un método para compresión de imágenes sin pérdidas, usando la transformada wavelet lifting con ajuste automático de coeficientes de filtros wavelets para una mayor compresión sin pérdidas. La propuesta se basa en reconocimiento de patrones utilizando clasificador 1-NN. Utilizando el reconocimiento de patrones se optimizan los coeficientes de los filtros lifting de manera global para cada imagen. La técnica propuesta fue aplicada para la compresión de imágenes de prueba y comparada con los filtros wavelets estándares CDF (2,2) y CDF (4,4), obteniendo resultados mejores en relación a la entropía obtenida para cada imágenes, así, como para el promedio general.Palabras clave-Compresión de imágenes sin pérdida, esquema lifting, wavelets, reconocimiento de patrones. Adjustment of Wavelet Filters for Image Compression Using Artificial IntelligenceAbstract-A method for lossless image compression using wavelet lifting transform with automatic adjustment of wavelet filter coefficients for better compression is presented. The proposal is based on pattern recognition by 1-NN classifier. Using the pattern recognition, the lifting filter coefficients are optimized globally for each image. The proposed technique was aplied to test images and the compression results were compared to the results produced by the standard CDF (2,2) and CDF (4,4) wavelet filters. The results obtained with the optimized wavelet filters are better in terms of the achieved entropy for all images, as well as for the overall performance.

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“…KNN classifier is implemented with changing the number of nearest neighbors (K), different measurement distances, and different rules. Finally, the new sample is classified by majority vote (Kotsiantis 2007; Song et al 2007;Starzacher and Rinner 2008).…”

Section: Classificationmentioning

confidence: 99%

Evaluation of local field potential signals in decoding of visual attention

Seif

Daliri

2015

Cogn Neurodyn

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In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.

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IKNN: Informative K-Nearest Neighbor Pattern Classification

Cited by 149 publications

References 11 publications

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

Evaluation of local field potential signals in decoding of visual attention

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