Sample-Based Extreme Learning Machine with Missing Data

Author:

Gao Hang1,Liu Xin-Wang1,Peng Yu-Xing1,Jian Song-Lei1

Affiliation:

1. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China

Abstract

Extreme learning machine (ELM) has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information). However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

Funder

Basic Research Development Program of China

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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5. The Effects of Missing Data Characteristics on the Choice of Imputation Techniques;Vietnam Journal of Computer Science;2020-03-20

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