Abstract
MOAFS: A Massive Online Analysis library for feature selection in data streams
Highlights
To perform different experiments on some of the most relevant feature selection algorithms proposed for data streams classification problems, the Massive Online Analysis Feature Selection (MOAFS) was created
MOAFS is a library for the Massive Online Analysis (MOA) (Bifet, Holmes, Kirkby, & Pfahringer, 2010) framework, and it is based on the MOAReduction (Ramírez-Gallego, Krawczyk, García, Woźniak, & Herrera, 2017) extension
It contains seven feature selection algorithms to be used as dimensionality reduction techniques in data streams classification problems, especially in the text-domain field, since they are not directly available on MOA
Summary
Each feature selection algorithm performs efficiently depending on different circumstances, such as data dimensionality (low, medium, high or ultra), speed rate, attribute type (nominal or numerical), number of classes, among others. To perform different experiments on some of the most relevant feature selection algorithms proposed for data streams classification problems, the Massive Online Analysis Feature Selection (MOAFS) was created. MOAFS is a library for the Massive Online Analysis (MOA) (Bifet, Holmes, Kirkby, & Pfahringer, 2010) framework, and it is based on the MOAReduction (Ramírez-Gallego, Krawczyk, García, Woźniak, & Herrera, 2017) extension. MOAFS is a package for MOA to perform feature selection in data streams classification problems
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