Browsing by Author "Chen, I-Ling"

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An Adaptive Rule Based on Unknown Pattern for Improving K-Nearest Neighbor Classifier
(2010-01-01)
Chen, I-Ling
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Pai, Kai-Chih
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Kuo, Bor-Chen
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Li, Cheng-Hsuan
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電控工程研究所
One of popular and simple pattern classification algorithms is the k-nearest neighbor rule. However, it often fails to work well when patterns of different classes overlap in some regions in the feature space. To overcome this problem, many researches strive for developing various adaptive or discriminatory metrics to improve its performance for classification, recently. In this paper, we proposed a simple adaptive nearest neighbor rule on distance measure for two objects. First one is to separate the overlapping data, and the second one is to avoid the influence of outliers. From the experimental results, our method is robust for the choice of the number of k and outperforms than k-nearest neighbor classifier.
APPLYING OPTIMAL ALGORITHM TO DATA-DEPENDENT KERNEL FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2010)
Chen, I-Ling
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Li, Cheng-Hsuan
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Kuo, Bor-Chen
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Huang, Hsiao-Yun
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電控工程研究所
In the kernel methods, it is very important to choose a proper kernel function to avoid overlapping data. Based this fact, in this paper we mainly utilize a unified kernel optimization framework on the hyperspectral image classification to augment the margin between different classes, and under the kernel optimization framework, to employ the Fisher discriminant criteria formulated in a pairwise manner as the objective functions to optimize the kernel function in Kernel-based nonparametric weighted feature extraction. The experimental results display the superiority of the optimizing kernel function over the RBF kernel function with 5-fold cross-validation method, especially, in the small sample size problem.
COMBINING ENSEMBLE TECHNIQUE OF SUPPORT VECTOR MACHINES WITH THE OPTIMAL KERNEL METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION
(2011)
Kuo, Bor-Chen
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Chen, I-Ling
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Li, Cheng-Hsuan
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Hung, Chih-Cheng
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電控工程研究所
In remote sensing researches, the curse of dimensionality is one greatly difficult classification problem. Many studies have demonstrated that multiple classifier systems, such as the random subspace method (RSM), can alleviate small sample size and high dimensionality concern and obtain more outstanding and robust results than a single classifier on extensive pattern recognition issues. A dynamic subspace method (DSM) was proposed for constructing component classifiers with adaptive subspaces to adjust the shortcomings of RSM based on resubstitution accuracy by applying each classifier. However, the performances of SVMs are based on choosing the proper kernel functions or proper parameters of a kernel function. The objective of this research is to develop a novel ensemble technique based on support vector machines (SVMs) via the optimal kernel method, and propose a novel subspace selection mechanism, named the kernel-based dynamic subspace method (KDSM), to improve DSM on automatically determining dimensionality and selecting component dimensions for diverse subspaces. Experimental results show a sound performance of classification on the famous hyperspectral images, Washington DC Mall.