Dimensionality reduction with missing values imputation

Autor:	Gahar, Rania Mkhinini, Arfaoui, Olfa, Hidri, Minyar Sassi, Alouane, Nejib Ben-Hadj
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Data Structures and Algorithms Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 2 figures, The first Computer science University of Tunis El Manar, PhD Symposium (CUPS'17), Tunisia, May 22-25, 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.00351 Zobrazit plný text záznamu View this record from Arxiv