A. I. Velasquez Pizarro, M. Zarzo
The case of tenders is a big data problem of high dimensionality and binary variables. This study empirically verifies, with bidding data, the relationship between Cronbach's Alpha coefficient and dimensionality reduction criteria in order to compare two multivariate models: i) Principal Component Analysis (PCA) for the continuous case and ii) Multiple Correspondence Analysis (MCA) with categorical data. In both models, it is proved that the reliability of the latent structure will be the key factor to determine the efficiency of the bidding classifier when it is in an exploratory or confirmatory stage. Finally, the most appropriate Machine Learning (ML) techniques are selected for segmentation and to achieve greater efficiency in tender classification.
Palabras clave: Multiple Correspondence Analysis (MCA), Principal Component Analysis (PCA), Public Procurement, Auction, Tender.
Programado
Análisis Multivariante
8 de noviembre de 2023 12:00
CC4: Sala 2