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Books > Computing & IT > Applications of computing > Artificial intelligence
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Data Mining and Computational Intelligence (Paperback, Softcover reprint of hardcover 1st ed. 2001)
Loot Price: R4,372
Discovery Miles 43 720
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Data Mining and Computational Intelligence (Paperback, Softcover reprint of hardcover 1st ed. 2001)
Series: Studies in Fuzziness and Soft Computing, 68
Expected to ship within 10 - 15 working days
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Many business decisions are made in the absence of complete
information about the decision consequences. Credit lines are
approved without knowing the future behavior of the customers;
stocks are bought and sold without knowing their future prices;
parts are manufactured without knowing all the factors affecting
their final quality; etc. All these cases can be categorized as
decision making under uncertainty. Decision makers (human or
automated) can handle uncertainty in different ways. Deferring the
decision due to the lack of sufficient information may not be an
option, especially in real-time systems. Sometimes expert rules,
based on experience and intuition, are used. Decision tree is a
popular form of representing a set of mutually exclusive rules. An
example of a two-branch tree is: if a credit applicant is a
student, approve; otherwise, decline. Expert rules are usually
based on some hidden assumptions, which are trying to predict the
decision consequences. A hidden assumption of the last rule set is:
a student will be a profitable customer. Since the direct
predictions of the future may not be accurate, a decision maker can
consider using some information from the past. The idea is to
utilize the potential similarity between the patterns of the past
(e.g., "most students used to be profitable") and the patterns of
the future (e.g., "students will be profitable").
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