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Contributions to classification with...

Li, Li.

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Contributions to classification with missing values and variable selection in clustering.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Contributions to classification with missing values and variable selection in clustering./
作者:	Li, Li.
面頁冊數:	115 p.
附註:	Adviser: James E. Gentle.
Contained By:	Dissertation Abstracts International68-12B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3294022
ISBN:	9780549381662

Contributions to classification with missing values and variable selection in clustering.
Li, Li.

Contributions to classification with missing values and variable selection in clustering. - 115 p.

Adviser: James E. Gentle.

Thesis (Ph.D.)--George Mason University, 2008.

Meta learning plays an important role in selecting classifiers. It builds connections between data characteristics and classification performance. The pattern of missing values is an important data characteristic in classification problems. I investigated the relationship between missing values and the performance of tree-based classification methods. The subject area is another important factor in meta learning since datasets from the same subject area share common characteristics. I focused attention on data of land cover mapping related to invasive species. The land cover datasets are classified using tree-based classification methods. My study shows that random forests classifier has a better performance in terms of classification accuracy than single tree methods and linear separation. Researchers have studied clusters defining by different regression models and use this to identify and explain the natural structure of data. The widely used maximum likelihood approach includes all covariates in all clusters, which makes it difficult to see clusters or other interesting trends in the data. I proposed a clustering method that integrates variable selection using the lasso regression (Tibshirani, 1996). The new method can simultaneously partition data into groups and find effective covariates within each cluster. I conducted Monte Carlo studies to illustrate and verify the new method.

ISBN: 9780549381662Subjects--Topical Terms:

517247
Statistics.

Contributions to classification with missing values and variable selection in clustering.
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520 $a Meta learning plays an important role in selecting classifiers. It builds connections between data characteristics and classification performance. The pattern of missing values is an important data characteristic in classification problems. I investigated the relationship between missing values and the performance of tree-based classification methods. The subject area is another important factor in meta learning since datasets from the same subject area share common characteristics. I focused attention on data of land cover mapping related to invasive species. The land cover datasets are classified using tree-based classification methods. My study shows that random forests classifier has a better performance in terms of classification accuracy than single tree methods and linear separation. Researchers have studied clusters defining by different regression models and use this to identify and explain the natural structure of data. The widely used maximum likelihood approach includes all covariates in all clusters, which makes it difficult to see clusters or other interesting trends in the data. I proposed a clustering method that integrates variable selection using the lasso regression (Tibshirani, 1996). The new method can simultaneously partition data into groups and find effective covariates within each cluster. I conducted Monte Carlo studies to illustrate and verify the new method.
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