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Advances in Data Analysis and Classification

Erschienen in:

21.03.2018 | Regular Article

An efficient random forests algorithm for high dimensional data classification

verfasst von: Qiang Wang, Thanh-Tung Nguyen, Joshua Z. Huang, Thuy Thi Nguyen

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 4/2018

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Abstract

In this paper, we propose a new random forest (RF) algorithm to deal with high dimensional data for classification using subspace feature sampling method and feature value searching. The new subspace sampling method maintains the diversity and randomness of the forest and enables one to generate trees with a lower prediction error. A greedy technique is used to handle cardinal categorical features for efficient node splitting when building decision trees in the forest. This allows trees to handle very high cardinality meanwhile reducing computational time in building the RF model. Extensive experiments on high dimensional real data sets including standard machine learning data sets and image data sets have been conducted. The results demonstrated that the proposed approach for learning RFs significantly reduced prediction errors and outperformed most existing RFs when dealing with high-dimensional data.

Vorheriger Artikel Ensemble feature selection for high dimensional data: a new method and a comparative study

Nächster Artikel Equi-Clustream: a framework for clustering time evolving mixed data

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Titel: An efficient random forests algorithm for high dimensional data classification
verfasst von: Qiang Wang
Thanh-Tung Nguyen
Joshua Z. Huang
Thuy Thi Nguyen
Publikationsdatum: 21.03.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 4/2018
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-018-0318-1

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