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Machine Learning Methods for High-Di...

Yang, Tao.

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Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data./
Author:	Yang, Tao.
Description:	83 p.
Notes:	Source: Masters Abstracts International, Volume: 52-02.
Contained By:	Masters Abstracts International52-02(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1543724
ISBN:	9781303312939

Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data.
Yang, Tao.

Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data. - 83 p.

Source: Masters Abstracts International, Volume: 52-02.

Thesis (M.S.)--Arizona State University, 2013.

Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.

ISBN: 9781303312939Subjects--Topical Terms:

523869
Computer science.

Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data.
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245 1 0 $a Machine Learning Methods for High-Dimensional Imbalanced Biomedical Data.
300 $a 83 p.
500 $a Source: Masters Abstracts International, Volume: 52-02.
500 $a Adviser: Jieping Ye.
502 $a Thesis (M.S.)--Arizona State University, 2013.
520 $a Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
590 $a School code: 0010.
650 4 $a Computer science. $3 523869
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710 2 $a Arizona State University. $b Computer Science. $3 1676136
773 0 $t Masters Abstracts International $g 52-02(E).
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1543724