Friday 24th of November 2017
 

Multi-objective Genetic Algorithm Based Selective Neural Networks Ensemble for Concentration Estimation of Indoor Air Pollutants Using Electronic Nose


Chaibou Kadri, Fengchun Tian, Lei Zhang, Xiongwei Peng and Xin Yin

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

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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


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