Multi-objective Genetic Algorithm Based Selective Neural Networks Ensemble for Concentration Estimation of Indoor Air Pollutants Using Electronic Nose
Neural networks ensemble or committee of neural networks is a
learning approach where many neural networks are combined to
solve a given problem. This approach has been proved to improve
the generalization performance of individual networks (base
networks), provided these networks are accurate enough while
being error-independent (diverse). In this paper, variance inflation
factor (VIF) is defined as diversity measure. A multi-objective
genetic algorithm (MOGA) with two objectives (ensemble error
and the new diversity metric) is used to select appropriate
members of the ensemble from a pool of trained neural networks.
The proposed method herein called MOGASEN(Multi Objective
Genetic Algorithm based Selective ensemble) and other popular
ensemble approaches were evaluated on data from an electronic
nose (E-Nose) for concentration estimation of four indoor air
pollutants (formaldehyde, benzene, toluene, and carbon
monoxide). Empirical results show that the proposed method,
while having higher capability in reducing the size of the
ensemble, was, in most cases, able to outperform other methods.
Keywords: Neural network ensemble, Electronic nose, variance inflation factor, Multi-objective genetic algorithm, air quality monitoring
Download Full-Text
ABOUT THE AUTHORS
Chaibou Kadri
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Fengchun Tian
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Lei Zhang
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Xiongwei Peng
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Xin Yin
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Chaibou Kadri
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Fengchun Tian
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Lei Zhang
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Xiongwei Peng
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China
Xin Yin
College of Communication Engineering, Chongqing University ShaZheng street 174, ShaPingBa district, Chongqing 400044, China