Any-Cost Discovery: Learning Optimal classification Rules

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dc.contributor.author Ni, A
dc.contributor.author Zhu, X
dc.contributor.author Zhang, C
dc.contributor.editor Zhang, S
dc.contributor.editor Jarvis, R
dc.date.accessioned 2009-11-09T02:45:51Z
dc.date.issued 2005-01
dc.identifier.citation AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence Proceedings, 2005, pp. 123 - 132
dc.identifier.isbn 3-540-30462-2
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/1966
dc.description.abstract Fully taking into account the hints possibly hidden in the absent data, this paper proposes a new criterion when selecting attributes for splitting to build a decision tree for a given dataset. In our approach, it must pay a certain cost to obtain an attribute value and pay a cost if a prediction is error. We use different scales for the two kinds of cost instead of the same cost scale defined by previous works. We propose a new algorithm to build decision tree with null branch strategy to minimize the misclassification cost. When consumer offers finite resources, we can make the best use of the resources as well as optimal results obtained by the tree. We also consider discounts in test costs when groups of attributes are tested together. In addition, we also put forward advice about whether it is worthy of increasing resources or not. Our results can be readily applied to real-world diagnosis tasks, such as medical diagnosis where doctors must try to determine what tests should be performed for a patient to minimize the misclassification cost in certain resources
dc.publisher Springer
dc.relation.isbasedon 10.1007/11589990_15
dc.title Any-Cost Discovery: Learning Optimal classification Rules
dc.type Conference Proceeding
dc.parent AI 2005: Advances in Artificial Intelligence, 18th Australian Joint Conference on Artificial Intelligence Proceedings
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 123 en_US
dc.identifier.endpage 132 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.conference Australasian Joint Conference on Artificial Intelligence
dc.conference.location Sydney, Australia en_US
dc.for 080105 Expert Systems
dc.personcode 011221
dc.percentage 100 en_US
dc.classification.name Expert Systems en_US
dc.classification.type FOR-08 en_US
dc.custom Australasian Joint Conference on Artificial Intelligence en_US
dc.date.activity 20051205 en_US
dc.date.activity 2005-12-05
dc.location.activity Sydney, Australia en_US
dc.description.keywords N/A en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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