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Causal Feature Selection
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Author(s): Walisson Ferreira Carvalho (Centro Universitario Una, Brazil) and Luis Zarate (Pontificia Universidade Catolica de Minas Gerais, Brazil)
Copyright: 2021
Pages: 16
Source title:
Integration Challenges for Analytics, Business Intelligence, and Data Mining
Source Author(s)/Editor(s): Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal) and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
DOI: 10.4018/978-1-7998-5781-5.ch007
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Abstract
Feature selection is a process of the data preprocessing task in business intelligence (BI), analytics, and data mining that urges for new methods that can handle with high dimensionality. One alternative that have been researched to deal with the curse of dimensionality is causal feature selection. Causal feature selection is not based on correlation, but the causality relationship among variables. The main goal of this chapter is to present, based on the issues identified on other methods, a new strategy that considers attributes beyond those that compounds the Markov blanket of a node and calculate the causal effect to ensure the causality relationship.
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