Dimensionality reduction with missing values imputation
Machine Learning
2017-07-04 v1 Data Structures and Algorithms
Machine Learning
Abstract
In this study, we propose a new statical approach for high-dimensionality reduction of heterogenous data that limits the curse of dimensionality and deals with missing values. To handle these latter, we propose to use the Random Forest imputation's method. The main purpose here is to extract useful information and so reducing the search space to facilitate the data exploration process. Several illustrative numeric examples, using data coming from publicly available machine learning repositories are also included. The experimental component of the study shows the efficiency of the proposed analytical approach.
Cite
@article{arxiv.1707.00351,
title = {Dimensionality reduction with missing values imputation},
author = {Rania Mkhinini Gahar and Olfa Arfaoui and Minyar Sassi Hidri and Nejib Ben-Hadj Alouane},
journal= {arXiv preprint arXiv:1707.00351},
year = {2017}
}
Comments
6 pages, 2 figures, The first Computer science University of Tunis El Manar, PhD Symposium (CUPS'17), Tunisia, May 22-25, 2017