1. NCCU Academic Hub
  2. 學術產出
  3. 資訊學院
  4. 期刊論文
  5. Item

學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Values
作者 徐國偉
Hsu,Kuo-Wei
貢獻者 資科系
關鍵詞 Bagging; missing values; multilayerperceptron; sequential minimal optimization
日期 2013-10
上傳時間 27-十二月-2013 18:06:28 (UTC+8)
摘要 Bagging is commonly used to improve the performance of a classification algorithm by first using bootstrap sampling on the given data set to train a number of classifiers and then using the majority voting mechanism to aggregate their outputs. However, the improvement would be limited in the situation where the given data set contains missing values and the algorithm used to train the classifiers is sensitive to missing values. We propose an extension of bagging that considers not only the weights of the classifiers in the voting process but also the incompleteness of the bootstrapped data sets used to train the classifiers. The proposed extension assigns a weight to each of the classifiers according to its classification performance and adjusts the weight of each of the classifiers according to the ratio of missing values in the data set on which it is trained. In experiments, we use two classification algorithms, two measures for weight assignment, and two functions for weight adjustment. The results reveal the potential of the proposed extension of bagging for working with classification algorithms sensitive to missing values to perform classification on data sets having small numbers of instances but containing relatively large numbers of missing alues.
關聯 International Journal of Information and Education Technology, 3(5),560-566
資料類型 article
DOI http://dx.doi.org/10.7763/IJIET.2013.V3.335
dc.contributor 資科系en_US
dc.creator (作者) 徐國偉zh_TW
dc.creator (作者) Hsu,Kuo-Weien_US
dc.date (日期) 2013-10en_US
dc.date.accessioned 27-十二月-2013 18:06:28 (UTC+8)-
dc.date.available 27-十二月-2013 18:06:28 (UTC+8)-
dc.date.issued (上傳時間) 27-十二月-2013 18:06:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/63086-
dc.description.abstract (摘要) Bagging is commonly used to improve the performance of a classification algorithm by first using bootstrap sampling on the given data set to train a number of classifiers and then using the majority voting mechanism to aggregate their outputs. However, the improvement would be limited in the situation where the given data set contains missing values and the algorithm used to train the classifiers is sensitive to missing values. We propose an extension of bagging that considers not only the weights of the classifiers in the voting process but also the incompleteness of the bootstrapped data sets used to train the classifiers. The proposed extension assigns a weight to each of the classifiers according to its classification performance and adjusts the weight of each of the classifiers according to the ratio of missing values in the data set on which it is trained. In experiments, we use two classification algorithms, two measures for weight assignment, and two functions for weight adjustment. The results reveal the potential of the proposed extension of bagging for working with classification algorithms sensitive to missing values to perform classification on data sets having small numbers of instances but containing relatively large numbers of missing alues.en_US
dc.format.extent 104 bytes-
dc.format.mimetype text/html-
dc.language.iso en_US-
dc.relation (關聯) International Journal of Information and Education Technology, 3(5),560-566en_US
dc.subject (關鍵詞) Bagging; missing values; multilayerperceptron; sequential minimal optimizationen_US
dc.title (題名) Weight-Adjusted Bagging of Classification Algorithms Sensitive to Missing Valuesen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.7763/IJIET.2013.V3.335-
dc.doi.uri (DOI) http://dx.doi.org/10.7763/IJIET.2013.V3.335-