Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems

By Lei Zhang, David Dapeng Zhang. IEEE Transactions on Instrumentation and Measurement, 2015

327 citations recorded by the source corpus.

This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples' recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.

Subfields: Artificial Intelligence, Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b89458a474fa27c2f399d93b08ba24855b75e4df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yudong Zhang
2. Jian Wang
3. Shui-hua Wang
4. Siyuan Lu
5. Arash Bahrammirzaee

In Biomedical Engineering

1. Yudong Zhang
2. Jian Wang
3. Shui-hua Wang
4. Siyuan Lu

In Electrical and Electronic Engineering

1. Yudong Zhang
2. Jian Wang
3. Shui-hua Wang
4. Siyuan Lu

Citations in the corpus

• A review on extreme learning machine (2021, methodology): “Later they modified this generalized domain adaptation extreme learning machine and tested it in the field of electronic nose sensor drift compensation [219] and visual knowledge domain adaptation [221], proving this framework ’ s efficiency and potential.”