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What is Local Learning

Integration Challenges for Analytics, Business Intelligence, and Data Mining
It is an approach that learn Bayesian Network limiting the DAG space to some variables that are potential candidates for local structures such as Markov Blanket or Parents and Children of a given target.
Published in Chapter:
Causal Feature Selection
Walisson Ferreira Carvalho (Centro Universitario Una, Brazil) and Luis Zarate (Pontificia Universidade Catolica de Minas Gerais, Brazil)
DOI: 10.4018/978-1-7998-5781-5.ch007
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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