Prediction of Missing Values in Adult Data Set of UCI Machine Learning : A Case of Study

• Autorzy: Luna Alejandra , Bello Mario , Hernandez Ana , Bonilla Edmundo

Identyfikatory

DOI

10.26348/znwwsi.22.7

• Języki publikacji: EN

Abstrakty

EN

These days, not having complete data of any kind can be a big problem for different organizations when making decisions. In this article, we propose to use Shannon entropy and information gain to predict and impute missing categorical data in any data set. It is detailed with an example of how entropy is applied and knows the level of uncertainty of each attribute value. Likewise, the imputation of the missing attributes is also carried out with other imputation techniques in the Adult data set of UCI Machine Learning to denote the advantages offered by the proposed methodology.

Słowa kluczowe

EN

Shannon theory; entropy; missing attributes; adult dataset; UCI Machine Learning

• Wydawca: Warszawska Wyższa Szkoła Informatyki
• Czasopismo: Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
• Rocznik: 2020
• Tom: nr 22
• Strony: 7--21
• Opis fizyczny: Bibliogr. 15 poz., tab.

Twórcy

autor

Luna Alejandra

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco

autor

Bello Mario

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco

autor

Hernandez Ana

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco

autor

Bonilla Edmundo

• Tecnológico Nacional de México, Instituto Tecnológico de Apizaco

Bibliografia

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