Work

Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing

Year: 2022

Type: article

Abstract: Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package is valuable since there is currently a large range of Machine Learning (ML) algorithms that can only be trained using numerical data (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with categorical data attributes. Currently, CANE offers three categorical to numeric transformation methods, namely: Percentage Categorical Pruned (PCP), Inverse Document Frequency (IDF) and a simpler One-Hot-Encoding method. Additionally, the CANE module is well documented with several code examples that can help in its adoption by non expert users.

Source: Software Impacts

DOI: 10.1016/j.simpa.2022.100359

Authors: Luís Miguel Matos, João Azevedo, Arthur Matta, André Pilastri, Paulo Cortez, Rui Mendes

Institutions: Centro ALGORITMI, University of Minho, Centro de Computação Gráfica

Language: English

Topic: Text and Document Classification Technologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $250

Funders: Fundação para a Ciência e a Tecnologia, European Regional Development Fund