Search Results

Now showing 1 - 3 of 3

Some of the metrics are blocked by your
consent settings
Classification of high-resolution NMR spectra based on complex wavelet domain feature selection and kernel-induced random forest
(2010-12-01)
Fan, Guangzhe
;
Wang, Zhou
;
Kim, Seoung Bum
;
Temiyasathit, Chivalai
High-resolution nuclear magnetic resonance (NMR) spectra contain important biomarkers that have potentials for early diagnosis of disease and subsequent monitoring of its progression. Traditional features extraction and analysis methods have been carried out in the original frequency spectrum domain. In this study, we conduct feature selection based on a complex wavelet transform by making use of its energy shift-insensitive property in a multi-resolution signal decomposition. A false discovery rate based multiple testing procedure is employed to identify important metabolite features. Furthermore, a novel kernel-induced random forest algorithm is used for the classification of NMR spectra based on the selected features. Our experiments with real NMR spectra showed that the proposed method leads to significant reduction in misclassification rate. © Springer-Verlag Berlin Heidelberg 2010.
Some of the metrics are blocked by your
consent settings
An effective classification procedure for diagnosis of prostate cancer in near infrared spectra
(2010-05-01)
Kim, Seoung Bum
;
Temiyasathit, Chivalai
;
Bensalah, Karim
;
Tuncel, Altug
;
Cadeddu, Jeffrey
The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy. © 2009 Elsevier Ltd. All rights reserved.
Some of the metrics are blocked by your
consent settings
Spatial prediction of ozone concentration profiles
(2009-09-01)
Temiyasathit, Chivalai
;
Kim, Seoung Bum
;
Park, Sun Kyoung
Ground level ozone is one of the major air pollutants in many urban areas. Ozone formation affects ecosystems and is known to be associated with many adverse health issues in humans. Effective modeling of ozone is a necessary step to develop a system to warn residents of high ozone levels. In the present study we propose a statistical procedure that uses multiscale and functional data analysis to improve the spatial prediction of ozone concentration profiles in the Dallas Fort Worth (DFW) area of Texas. This study uses daily eight-hour ozone concentrations and meteorological predictors during a period between 2003 and 2006 at 14 monitoring sites in the DFW area. Wavelet transformation was used as a means of multiscale data analysis, followed by functional modeling to reduce model complexity. Kriging was then used for spatial prediction. The experimental results with real data demonstrated that the proposed procedures achieved acceptable accuracy of spatial prediction. © 2009 Elsevier B.V. All rights reserved.

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results