Feature Selection with Limited Bit Depth Mutual Information for Embedded Systems

Proceedings

Bolón-Canedo, Verónica¹  Morán-Fernández, Laura²  Alonso-Betanzos, Amparo³ 

[1] Author to whom correspondence should be addressed.;CITIC, Universidade da Coruña, 15071 A Coruña, Spain;Presented at the XoveTIC Congress, A Coruña, Spain, 27–28 September 2018.

关键词: feature selection;    mutual information;    reduced precision;    embedded systems;    Big Data;   

DOI  :  10.3390/proceedings2181187

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big Data? Should it depend on the numerical representation of the machine? Since portable embedded systems have been growing in importance, there is also increased interest in implementing machine learning algorithms with a limited number of bits. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. In this work, we consider mutual information—one of the most common measures of dependence used in feature selection algorithms—with reduced precision parameters.

【 授权许可】

CC BY