Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 120 (2015) 249 – 252
EUROSENSORS 2015
An on-line reconfigurable classification algorithm improves the long-term stability of gas sensor arrays in case of faulty and drifting sensors G. Magna, F. Mosciano, E. Martinelli*, C. Di Natale Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
Abstract In this work, we illustrate an autonomous and real-time reconfigurable classifier. The algorithm starts from a non-adaptive classifier and evolves during the routine operation of sensors providing a dynamic optimization of the feature selection and refinement of classes’ distribution. The model has been tested on an experimental dataset and the results show that the algorithm may improve the resilience of classifiers in case of drifting and/or faulty sensors. The outcome of this studied case suggests that the algorithm might be able to enhance long-term performance almost independently from which classification model is considered. © © 2015 2015 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of EUROSENSORS 2015. Peer-review under responsibility of the organizing committee of EUROSENSORS 2015 Keywords: gas sensors, adaptive classifiers, fault tolerance, on-line feature selection
1. Introduction Volatile organic compounds (VOCs) recognition with sensor arrays requires the definition, from the sensor signals, of maximally informative and minimally redundant synthetic descriptors (features) [1]. Feature extraction is typically optimized during the training [2-3]; however, accidents like drift and fault can alter the information content of descriptors making the initial feature selection inadequate [4]. Although several algorithms have been introduced in literature to counteract the chemical sensor drift, few papers have investigated efficient solutions to reduce the decrease of performances when a sensor fault occurs in the online functioning [5-7]. Here, we introduce an algorithm that dynamically updates the selection of features considering the evolution of the information content. The properties of the algorithm are evaluated comparing the classification rates of three different classifiers (k-NN, PLS-DA, and LDA) (see table 1) applied to the original data and to data pre-processed by this reconfigurable algorithm.
* Corresponding author. Tel.: +390672597259; fax: +39062020519. E-mail address:
[email protected]
1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of EUROSENSORS 2015
doi:10.1016/j.proeng.2015.08.597
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G. Magna et al. / Procedia Engineering 120 (2015) 249 – 252
2. Algorithm Fig. 1 illustrates the main steps of the algorithm. The model utilizes a reservoir of samples, initially corresponding to the training data, to select a subset of features for each new sample presentation. The feature is selected only if probability of the class membership of the new sample is large. In other words, the criterion avoids including those features that make the sample either an outlier (too far from any class) or ambiguous (same distance from more that one class). Then class membership of the sample is calculated considering the selected features. Finally, the new sample is included in the set of templates of the class at which it is assigned. In order to limit the number of templates, a maximum number of templates per class is fixed, since the beginning, then oldest samples are removed to leave place to the newest templates. Table 1: Classification models considered in this work Classification model
Acronyms
k- Nearest Neighbour (k=3)
k-NN
Reconfigurable + k-NN
r k-NN
Partial least square
PLS-DA
Reconfigurable + PLS-DA
r PLS-DA
Linear Discriminant Analysis
LDA
Reconfigurable + LDA
r LDA
3. Experimental Details and Results. The experimental dataset has been generated by seven quartz crystal microbalances, each coated with a different metallo-porphyrin, and exposed to a random sequence of ethanol (at 2100 ppm), toluene (at 1400 ppm) and their mixtures [8]. The first 27 measures of the sequence were used to train the models and the other 84 to test. The scores plot of a PLS-DA model shown in Figure 2 offers a visualization of dataset with the class overlap due to the sensors drift. Fig. 2 shows the classification rates obtained by the classifiers with and without the proposed algorithm. Fifty fault sensor events were artificially simulated setting one of the sensors to random values after the 20th test sample. Fig. 3 shows the classification rate with and without the algorithm here discussed. The update of the distribution of class templates is efficient, at least in this dataset, to counteract the drift while the dynamic feature selection reduces the influence of outliers and fault sensor events ensuring the best choice of descriptors suitable for the current sensors conditions. The comparison of the classification rates shows that this approach not only improves the classification score (up to 30% in case of LDA) but interestingly it seems to level off the performance of the different classifiers in both drifting and faulting scenarios.
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Fig. 1: Schematic procedure of the dynamic reconfigurable approach
4. Conclusion. In this work, we have proposed real-time reconfigurable classifier that evolves during the online operation of sensors providing a dynamic feature selection and refinement of classes’ distribution. The potentialities of the proposed approach have been validated with an experimental dataset and with different standard classification models evidencing always its superior performances also in the sensor fault scenario.
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Figure 2: The scores plot of the first two LVs of the PLS-DA model provides a visualization of the dataset from where the sensors drift (the departure of test from training) can be evaluated.
Figure 3: A) Classification rates obtained by different standard classifiers with and without the dynamic approach; B) Means and standard deviations obtained in case of 50 random faults.
5. References [1] A. Hierleman, R. Gutierrez-Osuna. Chem. Rev. 2008,108, 563̢613. [2] A. Vergara, E. Llobet, E. Martinelli et al, Sens. Actuators B, 2007, 122, 219-226 [3] M.K. Muezzinoglu, A. Vergara, R. Huerta, et al., Sens. Actuators B, 2009, 137, 507-512. [4] A.C. Romain, J. Nicolais. Sens. Actuators B , 2010 , 146, 502̢506. [5] E. Martinelli, G. Magna, A. Vergara, C. Di Natale, Sens. Actuators B, 2014, 199, 83-92. [6] M. Padilla, A. Perera, I. Montoliu et al., Chem Lab, 2010, 100, 28-35. [7] E. Martinelli, G. Magna, S. De Vito et al, Sens. Actuators B, 2013, 177, 1017–1026. [8] E. Martinelli, M. Santonico, G. Pennazza, et al., Sens. Actuators B, 2011, 156, 753̢759
