Two layered Genetic Programming for mixed-attribute data classification
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- @Article{Jabeen2012416,
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author = "Hajira Jabeen and Abdul Rauf Baig",
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title = "Two layered Genetic Programming for mixed-attribute
data classification",
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journal = "Applied Soft Computing",
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volume = "12",
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number = "1",
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pages = "416--422",
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year = "2012",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2011.08.029",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494611003127",
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keywords = "genetic algorithms, genetic programming,
Classification, Mixed attribute data, Mixed type data
classification, Classifier",
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abstract = "The important problem of data classification spans
numerous real life applications. The classification
problem has been tackled by using Genetic Programming
in many successful ways. Most approaches focus on
classification of only one type of data. However, most
of the real-world data contain a mixture of categorical
and continuous attributes. In this paper, we present an
approach to classify mixed attribute data using Two
Layered Genetic Programming (L2GP). The presented
approach does not transform data into any other type
and combines the properties of arithmetic expressions
(using numerical data) and logical expressions (using
categorical data). The outer layer contains logical
functions and some nodes. These nodes contain the inner
layer and are either logical or arithmetic expressions.
Logical expressions give their Boolean output to the
outer tree. The arithmetic expressions give a real
value as their output. Positive real value is
considered true and a negative value is considered
false. These outputs of inner layers are used to
evaluate the outer layer which determines the
classification decision. The proposed classification
technique has been applied on various heterogeneous
data classification problems and found successful.",
- }
Genetic Programming entries for
Hajira Jabeen
Abdul Rauf Baig
Citations