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Evidence combination in medical data...

Mahajani, Gauri Anand.

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Evidence combination in medical data mining.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Evidence combination in medical data mining./
作者:	Mahajani, Gauri Anand.
面頁冊數:	51 p.
附註:	Source: Masters Abstracts International, Volume: 42-01, page: 0262.
Contained By:	Masters Abstracts International42-01.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1414757

Evidence combination in medical data mining.
Mahajani, Gauri Anand.

Evidence combination in medical data mining. - 51 p.

Source: Masters Abstracts International, Volume: 42-01, page: 0262.

Thesis (M.S.C.S.E.)--The University of Texas at Arlington, 2003.

In this work we apply a formal evidence combination technique for mining medical data. The mining task is classification of skin lesions and breast cancer cases. The goal is to combine beliefs from different sources of evidence, namely different classifiers, to arrive at a final classification. Dempster-Shafer theory takes into consideration evidences in the form of mathematically evaluated beliefs. It also considers uncertainty associated with beliefs. The evidences considered here are the beliefs obtained from three classifiers: k-nearest neighbor, Bayesian and Decision Tree. The classifier uncertainty based on its discriminative power is computed dynamically. Dempster's rule of combination combines the beliefs to arrive at a final decision. Our evaluation shows more accuracy than classifications based on individual beliefs. We study the circumstances under which the evidence combination approach improves classification.Subjects--Topical Terms:

626642
Computer Science.

Evidence combination in medical data mining.
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500 $a Source: Masters Abstracts International, Volume: 42-01, page: 0262.
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502 $a Thesis (M.S.C.S.E.)--The University of Texas at Arlington, 2003.
520 $a In this work we apply a formal evidence combination technique for mining medical data. The mining task is classification of skin lesions and breast cancer cases. The goal is to combine beliefs from different sources of evidence, namely different classifiers, to arrive at a final classification. Dempster-Shafer theory takes into consideration evidences in the form of mathematically evaluated beliefs. It also considers uncertainty associated with beliefs. The evidences considered here are the beliefs obtained from three classifiers: k-nearest neighbor, Bayesian and Decision Tree. The classifier uncertainty based on its discriminative power is computed dynamically. Dempster's rule of combination combines the beliefs to arrive at a final decision. Our evaluation shows more accuracy than classifications based on individual beliefs. We study the circumstances under which the evidence combination approach improves classification.
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