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2016 | OriginalPaper | Buchkapitel

Influence of Outliers Introduction on Predictive Models Quality

verfasst von : Mateusz Kalisch, Marcin Michalak, Marek Sikora, Łukasz Wróbel, Piotr Przystałka

Erschienen in: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery

Verlag: Springer International Publishing

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Abstract

The paper presents results of the research related to influence of the level of outliers in the data (train and test data considered separately) on the quality of a model prediction in a classification task. The set of 100 semi–artificial time series was taken into consideration, which independent variables was close to real ones, observed in a underground coal mining environment and dependent variable was generated with the decision tree. For every considered method (decision trees, naive bayes, logistic regression and kNN) a reference model was built (no outliers in the data) which quality was compared with the quality of two models: Out–Out (outliers in train and test data) and Non-out–Out (outliers only in test data). 50 levels of outliers in the data were considered, from 1 % to 50 %. Statistical comparison of models was done on the basis of sign test.

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Vorheriges Kapitel Performing and Visualizing Temporal Analysis of Large Text Data Issued for Open Sources: Past and Future Methods

Nächstes Kapitel Mining Rule-Based Knowledge Bases

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Titel: Influence of Outliers Introduction on Predictive Models Quality
verfasst von: Mateusz Kalisch
Marcin Michalak
Marek Sikora
Łukasz Wróbel
Piotr Przystałka
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery
Print ISBN: 978-3-319-34098-2

Electronic ISBN: 978-3-319-34099-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-34099-9_5

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