Genetic Programming Based Activity Recognition on a Smartphone Sensory Data Benchmark
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Xie:2014:CECa,
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title = "Genetic Programming Based Activity Recognition on a
Smartphone Sensory Data Benchmark",
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author = "Feng Xie and Andy Song and Vic Ciesielski",
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pages = "2917--2924",
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booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary
Computation",
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year = "2014",
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month = "6-11 " # jul,
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editor = "Carlos A. {Coello Coello}",
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address = "Beijing, China",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, Genetic programming,
Classification, clustering and data analysis,
Real-world applications",
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DOI = "doi:10.1109/CEC.2014.6900635",
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abstract = "Activity recognition from smart phone sensor inputs is
of great importance to enhance user experience. Our
study aims to investigate the applicability of Genetic
Programming (GP) approach on this complex real world
problem. Traditional methods often require substantial
human efforts to define good features. Moreover the
optimal features for one type of activity may not be
suitable for another. In comparison, our GP approach
does not require such feature extraction process,
hence, more suitable for complex activities where good
features are difficult to be pre-defined. To facilitate
this study we therefore propose a benchmark of activity
data collected from various smartphone sensors, as
currently there is no existing publicly available
database for activity recognition. In this study, a
GP-based approach is applied to nine types of activity
recognition tasks by directly taking raw data instead
of features. The effectiveness of this approach can be
seen by the promising results. In addition our
benchmark data provides a platform for other machine
learning algorithms to evaluate their performance on
activity recognition.",
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notes = "WCCI2014",
- }
Genetic Programming entries for
Feng Xie
Andy Song
Victor Ciesielski
Citations