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Cardoso, M. G. M. S. (2012). Logical discriminant models. In Luiz Moutinho. Kun-Huang Huarng (Ed.), Quantitative modelling in marketing and management. (pp. 223-254).: World Scientific Publishing.

M. M. Cardoso,  "Logical discriminant models", in Quantitative modelling in marketing and management, Luiz Moutinho. Kun-Huang Huarng, Ed., World Scientific Publishing, 2012, pp. 223-254

@incollection{cardoso2012_1732205216690,
	author = "Cardoso, M. G. M. S.",
	title = "Logical discriminant models",
	chapter = "",
	booktitle = "Quantitative modelling in marketing and management",
	year = "2012",
	volume = "",
	series = "",
	edition = "",
	pages = "223-223",
	publisher = "World Scientific Publishing",
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	url = "http://www.scopus.com/inward/record.url?eid=2-s2.0-84973442800&partnerID=MN8TOARS"
}

TY  - CHAP
TI  - Logical discriminant models
T2  - Quantitative modelling in marketing and management
AU  - Cardoso, M. G. M. S.
PY  - 2012
SP  - 223-254
DO  - 10.1142/9789814407724_0008
UR  - http://www.scopus.com/inward/record.url?eid=2-s2.0-84973442800&partnerID=MN8TOARS
AB  - Discriminant analysis aims to classify multivariate observations into a priori defined classes and explain the differences between them. Logical discriminant type models - trees and rules - emerge in this context and within the data mining framework as powerful predictive tools that generate very easy to interpret results. Moreover, they generally provide means to deal with explantory variables of different measurement levels (quantitative and qualitative), good handling of missing data and robustness. These characteristics are particularly appreciated in management decision support. Discriminant analysis basic concepts and trees and rules algorithms are presented in this chapter. General issues concerning the evaluation of discriminant analysis and the key concept of diversity are outlined first. Tree algorithms, successively dividing a set of observations to conquer less diversity and increase accuracy, are described next. The induction of propositional rules, whether based on trees or yielded by a set covering approach, is also described. An application in retail and specific algorithms (e.g., CART, C5, CN2 and LEM) illusrate the logical discriminant methodologies. Final remarks provide a contextualisation of the diversity of contributions in this domain. 
ER  -