Sparse Bayesian ELM Handling with Missing Data for Multi-class Classification

Abstract

Extreme learning machine (ELM) is a successful machine learning approach for its extremely fast training speed and good generalization performance. The sparse Bayesian ELM (SBELM) approach, which is a variant of ELM, can result in a more accurate and compact model. However, SBELM can not deal with the missing data problem in its standard form. To solve this problem, we design two novel methods, additive models for missing data (AMMD) and self-adjusting neuron state for missing data (SANSMD), by adjusting the calculation of outputs of the hidden layers in SBELM. Experimental results on several data sets from the UCI repository indicate that the proposed modified SBELM methods have significant advantages: high accuracy and good generalization performance compared with several other existing methods. Moreover, the proposed methods enrich ELM with new tools to solve missing data problem for multi-class classification even with up to 50% of the features missing in the input data.