東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Learning cost-sensitive diagnostic p...

Zubek, Valentina Bayer.

Linked to FindBook

Google Book

Amazon

博客來

Learning cost-sensitive diagnostic policies from data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning cost-sensitive diagnostic policies from data./
Author:	Zubek, Valentina Bayer.
Description:	247 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3920.
Contained By:	Dissertation Abstracts International64-08B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3103580
ISBN:	0496511489

Learning cost-sensitive diagnostic policies from data.
Zubek, Valentina Bayer.

Learning cost-sensitive diagnostic policies from data. - 247 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3920.

Thesis (Ph.D.)--Oregon State University, 2003.

In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body temperature, weight, etc.) and laboratory measurements (white blood count, CT scan, MRI scan, etc.) in order to determine the disease of the patient. A diagnostic policy is a complete description of the decision-making actions of a diagnostician under all possible circumstances. This dissertation studies the problem of learning diagnostic policies from training examples. An optimal diagnostic policy is one that minimizes the expected total cost of diagnosing a patient, where the cost is composed of two components: (a) measurement costs (the costs of performing various diagnostic tests) and (b) misdiagnosis costs (the costs incurred when the patient is incorrectly diagnosed). The optimal policy must perform diagnostic tests until further measurements do not reduce the expected total cost of diagnosis.

ISBN: 0496511489Subjects--Topical Terms:

626642
Computer Science.

Learning cost-sensitive diagnostic policies from data.
LDR:03321nmm 2200301 4500 001 1838399
005 20050526083750.5
008 130614s2003 eng d
020 $a 0496511489
035 $a (UnM)AAI3103580
035 $a AAI3103580
040 $a UnM $c UnM
100 1 $a Zubek, Valentina Bayer. $3 1926817
245 1 0 $a Learning cost-sensitive diagnostic policies from data.
300 $a 247 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3920.
500 $a Adviser: Thomas G. Dietterich.
502 $a Thesis (Ph.D.)--Oregon State University, 2003.
520 $a In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body temperature, weight, etc.) and laboratory measurements (white blood count, CT scan, MRI scan, etc.) in order to determine the disease of the patient. A diagnostic policy is a complete description of the decision-making actions of a diagnostician under all possible circumstances. This dissertation studies the problem of learning diagnostic policies from training examples. An optimal diagnostic policy is one that minimizes the expected total cost of diagnosing a patient, where the cost is composed of two components: (a) measurement costs (the costs of performing various diagnostic tests) and (b) misdiagnosis costs (the costs incurred when the patient is incorrectly diagnosed). The optimal policy must perform diagnostic tests until further measurements do not reduce the expected total cost of diagnosis.
520 $a The dissertation investigates two families of algorithms for learning diagnostic policies: greedy methods and methods based on the AO* algorithm for systematic search. Previous work in supervised learning constructed greedy di agnostic policies that either ignored all costs or considered only measurement costs or only misdiagnosis costs. This research recognizes the practical importance of costs incurred by performing measurements and by making incorrect diagnoses and studies the tradeoff between them. This dissertation develops improved greedy methods. It also introduces a new family of learning algorithms based on systematic search. Systematic search has previously been regarded as computationally infeasible for learning diagnostic policies. However, this dissertation describes an admissible heuristic for AO* that enables it to prune large parts of the search space. In addition, the dissertation shows that policies with better performance on an independent test set are learned when the AO* method is regularized in order to reduce overfitting.
520 $a Experimental studies on benchmark data sets show that in most cases the systematic search methods give better diagnostic policies than the greedy methods. Hence, these AO*-based methods are recommended for learning diagnostic policies that seek to minimize the expected total cost of diagnosis.
590 $a School code: 0172.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
690 $a 0984
690 $a 0800
710 2 0 $a Oregon State University. $3 625720
773 0 $t Dissertation Abstracts International $g 64-08B.
790 1 0 $a Dietterich, Thomas G., $e advisor
790 $a 0172
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3103580