Keynote talks: Evolutionary feature selection and dimensionality reduction
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- @InProceedings{Zhang:2017:APSIES,
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author = "Mengjie Zhang",
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booktitle = "2017 21st Asia Pacific Symposium on Intelligent and
Evolutionary Systems (IES)",
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title = "Keynote talks: Evolutionary feature selection and
dimensionality reduction",
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year = "2017",
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pages = "ix--xii",
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size = "1 page",
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abstract = "In data mining and machine learning, many real-world
problems such as bio-data classification and biomarker
detection, image analysis, text mining often involve a
large number of features/attributes. However, not all
the features are essential since many of them are
redundant or even irrelevant, and the useful features
are typically not equally important. Using all the
features for classification or other data mining tasks
typically does not produce good results due to the big
dimensionality and the large search space. This problem
can be solved by feature selection to select a small
subset of original (relevant) features or feature
construction to create a smaller set of high-level
features using the original low-level features. Feature
selection and construction are very challenging tasks
due to the large search space and feature interaction
problems. Exhaustive search for the best feature subset
of a given dataset is practically impossible in most
situations. A variety of heuristic search techniques
have been applied to feature selection and
construction, but most of the existing methods still
suffer from stagnation in local optima and/or high
computational cost. Due to the global search potential
and heuristic guidelines, evolutionary computation
techniques such as genetic algorithms, genetic
programming, particle swarm optimisation, ant colony
optimisation, differential evolution and evolutionary
multiobjective optimisation have been recently used for
feature selection and construction for dimensionality
reduction, and achieved great success. Many of these
methods only select/construct a small number of
important features, produce higher accuracy, and
generated small models that are efficient on unseen
data. Evolutionary computation techniques have now
become an important means for handle big dimensionality
and feature selection and construction. The talk will
introduce the general framework within which
evolutionary feature selection and construction can be
studied and applied, sketching a schematic taxonomy of
the field and providing examples of successful
real-world applications. The application areas to be
covered will include bio-data classification and
biomarker detection, image analysis and object
recognition and pattern classification, symbolic
regression, network security and intrusion detection,
and text mining. EC techniques to be covered will
include genetic algorithms, genetic programming,
particle swarm optimisation, differential evolution,
ant colony optimisation, artificial bee colony
optimisation, and evolutionary multi-objective
optimisation. We will show how such evolutionary
computation techniques can be effectively applied to
feature selection/construction and dimensionality
reduction and provide promising results.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "
doi:10.1109/IESYS.2017.8233551",
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month = nov,
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notes = "Also known as \cite{8233551}",
- }
Genetic Programming entries for
Mengjie Zhang
Citations