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arXiv · 1707.00351

Dimensionality reduction with missing values imputation

Rania Mkhinini Gahar·Olfa Arfaoui·Minyar Sassi Hidri·Nejib Ben-Hadj Alouane
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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.

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BibTeXRIS

Rania Mkhinini Gahar, Olfa Arfaoui, Minyar Sassi Hidri, Nejib Ben-Hadj Alouane. 2017-07-02. Dimensionality reduction with missing values imputation. https://arxiv.org/abs/1707.00351

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