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Moving Object Detection and Tracking Based on Three-Frame Difference and Background Subtraction with Laplace Filter Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Complexity of Rule Sets Induced by Characteristic Sets and Generalized Maximal Consistent Blocks

Authors : Patrick G. Clark, Cheng Gao, Jerzy W. Grzymala-Busse, Teresa Mroczek, Rafal Niemiec

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

We study mining incomplete data sets with two interpretations of missing attribute values, lost values and “do not care” conditions. For data mining we use characteristic sets and generalized maximal consistent blocks. Additionally, we use three types of probabilistic approximations, lower, middle and upper, so altogether we apply six approaches to data mining. Since it was shown that an error rate, associated with such data mining is not universally smaller for any approach, we decided to compare complexity of induced rule sets. Therefore, our objective is to compare six approaches to mining incomplete data sets in terms of complexity of induced rule sets. We conclude that there are statistically significant differences between these approaches.

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previous chapter An Efficient Prototype Selection Algorithm Based on Dense Spatial Partitions
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Metadata
Title
Complexity of Rule Sets Induced by Characteristic Sets and Generalized Maximal Consistent Blocks
Authors
Patrick G. Clark
Cheng Gao
Jerzy W. Grzymala-Busse
Teresa Mroczek
Rafal Niemiec
Copyright Year
2018
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-91261-5

Electronic ISBN: 978-3-319-91262-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-91262-2_27

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