Missing Features Reconstruction and Its Impact on Classification Accuracy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00331729" target="_blank" >RIV/68407700:21240/19:00331729 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-22744-9_16" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-22744-9_16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-22744-9_16" target="_blank" >10.1007/978-3-030-22744-9_16</a>

Result language

angličtina

Original language name

Missing Features Reconstruction and Its Impact on Classification Accuracy

Original language description

In real-world applications, we can encounter situations when a well-trained model has to be used to predict from a damaged dataset. The damage caused by missing or corrupted values can be either on the level of individual instances or on the level of entire features. Both situations have a negative impact on the usability of the model on such a dataset. This paper focuses on the scenario where entire features are missing which can be understood as a specific case of transfer learning. Our aim is to experimentally research the influence of various imputation methods on the performance of several classification models. The imputation impact is researched on a combination of traditional methods such as k-NN, linear regression, and MICE compared to modern imputation methods such as multi-layer perceptron (MLP) and gradient boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two approaches to using them for multiple features imputation. The experiments were performed on both real world and artificial datasets with continuous features where different numbers of features, varying from one feature to 50%, were missing. The results show that MICE and linear regression are generally good imputers regardless of the conditions. On the other hand, the performance of MLP and XGBT is strongly dataset dependent. Their performance is the best in some cases, but more often they perform worse than MICE or linear regression.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computational Science – ICCS 2019

ISBN

978-3-030-22744-9

ISSN

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e-ISSN

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Number of pages

14

Pages from-to

207-220

Publisher name

Springer, Cham

Place of publication

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Event location

Faro

Event date

Jun 12, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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