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2 2020

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

Luna Alejandra, Bello Mario, Hernandez Ana, Bonilla Edmundo

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

2020

nr 22

7--21

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.

Novel profile’s selection algorithm using AI

Bello Mario, Luna Alejandra, Bonilla Edmundo, Hernandez Crispin, Pedroza Blanca, Portilla Alberto

Applied Computer Science

2020

Vol. 16, no 1

18--32

In order to better understand the job requirements, recruitment processes, and hiring processes it is needed to know the people skills. For a recruiter this entails analyzing and comparing the curricula of each available candidate and determining the most appropriate candidate that the activities that are required by the position. This process must be carried in the shortest length of time possible. In this paper, an algorithm is proposed to identify those candidates, either workers or college graduates.