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| j9登录网址的公告 |
| 青年学术报告cost-sensitive feature value acquisition in testing
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计算机软件新技术国家重点实验室
摘 要：
in medical diagnosis doctors must often determine what medical tests (e.g., x-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. in this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. medical tests are like attributes in machine learning whose values may be obtained at a cost (attribute cost), and misdiagnoses are like misclassifications which may also incur a cost (misclassification cost). we first propose a lazy decision tree learning algorithm that minimizes the sum of attribute costs and misclassification costs. then we design several novel “test strategies” that can request to obtain values of unknown attributes at a cost (similar to doctors’ ordering of medical tests at a cost) in order to minimize the total cost for test examples (new patients). these test strategies correspond to different situations in real-world diagnoses. we empirically evaluate these test strategies, and show that they are effective and outperform previous methods. our results can be readily applied to real-world diagnosis tasks. a case study on heart disease is given throughout the paper.
报告人简介:
victor s. sheng received the m.sc. degree from the university of new brunswick, fredericton, nb, canada, and the ph.d. degree from the university of western ontario, london, on, canada, both in computer science, in 2003 and 2007, respectively.
he is an associate professor of computer science and the founding director of data analytics laboratory at university of central arkansas. his research interests include data mining, machine learning, crowdsourcing, and related applications in business, industry, medical informatics, and software engineering. he has published more than 150 research papers in conferences and journals of machine learning and data mining. most papers are published in top journals and conferences in data science, such as pami, tnnls, tkde, jmlr, aaai, kdd, ijcai, and acmmm.
prof. sheng is a senior member of ieee. he is a conference organizer for several conferences, and an editorial board member for several journals. he also is a spc and pc member for many international conferences (such as ijcai, aaai, and kdd) and a reviewer of more than twenty international journals (such as pami, tnnls, tkde, and jmlr). he was the recipient of the best paper award runner up from kdd’08, the best paper award from icdm’11, the best student paper award finalist from wise’15, the best paper award from icccs’18, the google student award winner of the 3rd annual machine learning symposium 2008, and the best poster award of the uw and ieee kitchener-waterloo section joint workshop on knowledge and data mining (2006).
时间：7月18日 10:30-11:30
地点：计算机科学技术楼230室
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