Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modelling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modelling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies.
Article originally published in ‘Algorithms’, V.1 (2008), n. 2, pp. 130-152, //www.mdpi.com/1999-4893/1/2/130. © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (//creativecommons.org/licenses/by/3.0/).