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- W4385900101 abstract "In this paper we introduce the use of time-resolved variables to represent the evolution of categorical variables through time. Traditionally, in data science the presence or absence of categorical variables are treated as 1 or 0 with One-Hot Encoding, and then aggregated with compression techniques, such as Principal Components. We annotated time-resolved variables on the Seshat dataset as sequences of categorical features and we compare them to the same categorical features treated with one-hot encoding and Principal Component compression. We find that time-resolved variables are better predictors of the evolution of social scale and social hierarchy, but not of government specialization. We discuss advantages and limitation of the usage of time-resolved variables for the computational analysis of cultural evolution." @default.
- W4385900101 created "2023-08-18" @default.
- W4385900101 creator A5019996096 @default.
- W4385900101 date "2023-08-04" @default.
- W4385900101 modified "2023-09-26" @default.
- W4385900101 title "Feature Engineering for Quantitative Analysis of Cultural Evolution" @default.
- W4385900101 doi "https://doi.org/10.31235/osf.io/aj8xk" @default.
- W4385900101 hasPublicationYear "2023" @default.
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