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Erschienen in:
Fuzzy Analysis of Sentiment Terms for Topic Detection Process in Social Networks Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Missing Data Imputation by LOLIMOT and FSVM/FSVR Algorithms with a Novel Approach: A Comparative Study

verfasst von : Fatemeh Fazlikhani, Pegah Motakefi, Mir Mohsen Pedram

Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

Verlag: Springer International Publishing

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Abstract

Missing values occurrence is an inherent part of collecting data sets in real world’s problems. This issue, causes lots of ambiguities in data analysis while processing data sets. Therefore, implementing methods which can handle missing data issues are critical in many fields, in order to providing accurate, efficient and valid analysis.
In this paper, we proposed a novel preprocessing approach that estimates and imputes missing values in datasets by using LOLIMOT and FSVM/FSVR algorithms, which are state-of-the-art algorithms. Classification accuracy, is a scale for comparing precision and efficiency of presented approach with some other well-known methods. Obtained results, show that proposed approach is the most accurate one.

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Vorheriges Kapitel Empirical Comparison of Distances for Agglomerative Hierarchical Clustering
Nächstes Kapitel Two Modifications of the Automatic Rule Base Synthesis for Fuzzy Control and Decision Making Systems
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Metadaten
Titel
Missing Data Imputation by LOLIMOT and FSVM/FSVR Algorithms with a Novel Approach: A Comparative Study
verfasst von
Fatemeh Fazlikhani
Pegah Motakefi
Mir Mohsen Pedram
Copyright-Jahr
2018
Buch
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

Print ISBN: 978-3-319-91475-6

Electronic ISBN: 978-3-319-91476-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-91476-3_46