Genetic programming for multiple-feature construction on high-dimensional classification
Created by W.Langdon from
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- @Article{TRAN:2019:patcog,
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author = "Binh Tran and Bing Xue and Mengjie Zhang",
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title = "Genetic programming for multiple-feature construction
on high-dimensional classification",
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journal = "Pattern Recognition",
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year = "2019",
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volume = "93",
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pages = "404--417",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Feature
construction, Classification, Class dependence,
High-dimensional data",
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ISSN = "0031-3203",
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URL = "http://www.sciencedirect.com/science/article/pii/S0031320319301815",
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DOI = "doi:10.1016/j.patcog.2019.05.006",
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abstract = "Data representation is an important factor in deciding
the performance of machine learning algorithms
including classification. Feature construction (FC) can
combine original features to form high-level ones that
can help classification algorithms achieve better
performance. Genetic programming (GP) has shown promise
in FC due to its flexible representation. Most GP
methods construct a single feature, which may not scale
well to high-dimensional data. This paper aims at
investigating different approaches to constructing
multiple features and analysing their effectiveness,
efficiency, and underlying behaviours to reveal the
insight of multiple-feature construction using GP on
high-dimensional data. The results show that
multiple-feature construction achieves significantly
better performance than single-feature construction. In
multiple-feature construction, using multi-tree GP
representation is shown to be more effective than using
the single-tree GP thanks to the ability to consider
the interaction of the newly constructed features
during the construction process. Class-dependent
constructed features achieve better performance than
the class-independent ones. A visualisation of the
constructed features also demonstrates the
interpretability of the GP-based FC approach, which is
important to many real-world applications",
- }
Genetic Programming entries for
Binh Ngan Tran
Bing Xue
Mengjie Zhang
Citations