Name: data mining and computational intelligence (en Inglés)
Price: 484.47 PEN
Availability: InStock
Author: Kandel, Abraham ; Last, Mark ; Bunke, Horst
ISBN: 9783790824841

portada data mining and computational intelligence (en Inglés)

data mining and computational intelligence (en Inglés)

Formato

Libro Físico

Ilustrado por

Editorial

Autor

Kandel, Abraham ; Last, Mark ; Bunke, Horst

Año

2010

Idioma

Inglés

N° páginas

356

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 15.6 x 2.0 cm

Peso

0.52 kg.

ISBN

3790824844

ISBN13

9783790824841

Categorías

Empresa y gestión
Software empresarial
Bases de datos
Inteligencia artificial

data mining and computational intelligence (en Inglés)

Mark Last (Ilustrado por) · Abraham Kandel (Ilustrado por) · Horst Bunke (Ilustrado por) · Physica-Verlag · Tapa Blanda

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Reseña del libro "data mining and computational intelligence (en Inglés)"

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").