Abstract
In this paper, we propose a robust sensor array optimization method based on sparse learning for multi-feature fusion data classification. The proposed approach contains three key characteristics. First, it considers the intrinsic group structure among features by combining an \(\ell _{F,1}\) norm regularizer design and least squares regression framework. Second, in sensor selection, insignificant feature groups can be eliminated by grouped row sparse coefficients generated by the model, while the \(\varepsilon \)-dragging trick is introduced to improve the classification ability. Third, an efficient alternating iteration algorithm is presented to optimize the convex objective function. The results compared with the other classical methods on gas sensor array data sets demonstrate that the proposed method can effectively reduce the number of sensors with higher classification accuracy.
This work is founded by the Natural Science Foundation of China (No. 62171066).
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References
Huerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N.F., Rodriguez-Lujan, I.: Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring. Chemometr. Intell. Lab. Syst. 157, 169–176 (2016)
Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. (CSUR) 50(6), 1–45 (2017)
Liu, B., et al.: Lung cancer detection via breath by electronic nose enhanced with a sparse group feature selection approach. Sens. Actuators B Chem. 339, 129896 (2021)
Nie, F., Huang, H., Cai, X., Ding, C.: Efficient and robust feature selection via joint \(\ell \)2, 1-norms minimization. Adv. Neural Information Process. Syst. 23, 1813–1821 (2010)
Röck, F., Barsan, N., Weimar, U.: Electronic nose: current status and future trends. Chem. Rev. 108(2), 705–725 (2008)
Rodriguez-Lujan, I., Fonollosa, J., Vergara, A., Homer, M., Huerta, R.: On the calibration of sensor arrays for pattern recognition using the minimal number of experiments. Chemometr. Intell. Lab. Syst. 130, 123–134 (2014)
Saha, P., Ghorai, S., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N.: Optimization of sensor array in electronic nose by combinational feature selection method. In: Mason, A., Mukhopadhyay, S.C., Jayasundera, K.P., Bhattacharyya, N. (eds.) Sensing Technology: Current Status and Future Trends II. SSMI, vol. 8, pp. 189–205. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02315-1_9
Scott, S.M., James, D., Ali, Z.: Data analysis for electronic nose systems. Microchimi. Acta 156(3), 183–207 (2006). https://doi.org/10.1007/s00604-006-0623-9
Sun, H., et al.: Sensor array optimization of electronic nose for detection of bacteria in wound infection. IEEE Trans. Ind. Electron. 64(9), 7350–7358 (2017)
Vergara, A., Vembu, S., Ayhan, T., Ryan, M.A., Homer, M.L., Huerta, R.: Chemical gas sensor drift compensation using classifier ensembles. Sens. Actuators B Chem. 166, 320–329 (2012)
Wei, G., Zhao, J., Yu, Z., Feng, Y., Li, G., Sun, X.: An effective gas sensor array optimization method based on random forest. In: 2018 IEEE SENSORS, pp. 1–4. IEEE (2018)
Xiang, S., Nie, F., Meng, G., Pan, C., Zhang, C.: Discriminative least squares regression for multiclass classification and feature selection. IEEE Trans. Neural Netw Learn. Syst. 23(11), 1738–1754 (2012)
Yan, J., et al.: Electronic nose feature extraction methods: A review. Sensors 15(11), 27804–27831 (2015)
Zhou, J., Welling, C.M., Kawadiya, S., Deshusses, M.A., Grego, S., Chakrabarty, K.: Sensor-array optimization based on mutual information for sanitation-related malodor alerts. In: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2019)
Ziyatdinov, A., Fonollosa, J., Fernandez, L., Gutierrez-Galvez, A., Marco, S., Perera, A.: Bioinspired early detection through gas flow modulation in chemo-sensory systems. Sens. Actuators B Chem. 206, 538–547 (2015)
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Zhao, L., Tian, F., Qian, J., Liu, R., Jiang, A. (2022). Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_15
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