Abstract
Data reduction is an important pre-processing step to both supervised and unsupervised machine learning problems. In this paper, we investigate, in a first part, the two existing strategies for data reduction which are feature selection (FS) and dimensionality reduction (DR). In a second part, we study the impact of different data reduction methods on supervised machine learning in terms of classification accuracy and computational costs. In fact, we compare, in the one hand, the generated subsets of attributes by filter and wrapper algorithms as well as new variables constructed by two variants of a DR method. In the other hand, we compare the classification achieved on initial data set, reduced data sets and also on successively reduced size of the considered data sets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.