• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.276-284
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• 2012

Classification algorithms generally assume that the data is complete. However, missing values are common in real data sets due to various reasons. In this paper, we propose to use locally linear reconstruction (LLR) for missing value imputation to improve the classification performance when missing values exist. We first investigate how much missing values degenerate the classification performance with regard to various missing ratios. Then, we compare the proposed missing value imputation (LLR) with three well-known single imputation methods over three different classifiers using eight data sets. The experimental results showed that (1) any imputation methods, although some of them are very simple, helped to improve the classification accuracy; (2) among the imputation methods, the proposed LLR imputation was the most effective over all missing ratios, and (3) when the missing ratio is relatively high, LLR was outstanding and its classification accuracy was as high as the classification accuracy derived from the compete data set.

• 한국전산유체공학회:학술대회논문집
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• 2003.08a
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• pp.31-36
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• 2003

A field reconstruction scheme for a cell centered finite volume method on unstructured meshes is developed. Regardless of mesh quality, this method is exact within a machine accuracy if the solution is linear, which means it has full second order accuracy. It does not have any limitation on cell shape except convexity of the cells and recovers standard discretization stencils at structured orthogonal grids. Accuracy comparisons with other popular reconstruction schemes are performed on a simple example.

• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.71-76
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• 2004

An extension of our recently developed locally linear reconstruction scheme to 2 dimensional incompressible flow solver is presented. The solver is based on a semi-implicit fractional step method in which the convective term is discretized by Adams-Bashforth method and the diffusion term by Crank-Nicolson method. Several numerical examples are tested to demonstrate the mesh type independent accuracy of the solver, which include decaying vortex flow, square cavity flow, and flow around a circular cylinder. The above examples are solved on quadrilateral or hybrid meshes. For all numerical examples, we obtained reasonable results.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.547-552
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• 2007

This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.