ELM-Based Ensemble Classifier for Gas Sensor Array Drift Dataset
Much work has been done on classification for the past fifteen years to develop adapted techniques and robust algorithms. The problem of data correction in the presence of simultaneous sources of drift, other than sensor drift, should also be investigated
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Abstract Much work has been done on classification for the past fifteen years to develop adapted techniques and robust algorithms. The problem of data correction in the presence of simultaneous sources of drift, other than sensor drift, should also be investigated, since it is often the case in practical situations. ELM is a competitive machine learning technique, which has been applied in different domains for classification. In this paper, ELM with different activation functions has been implemented for gas sensor array drift dataset. The experimental results show that the ELM with bipolar function classifies the drift dataset with an average accuracy of 96 % than the other function. The proposed method is compared with SVM. Keywords ELM
Ensembles Gas sensor array drift dataset Bipolar
1 Introduction The past decade has seen a significant increase in the application of multi-sensor arrays to gas classification and quantification. The idea to combine an array of sensors with a pattern recognition algorithm to improve the selectivity of the single D. Arul Pon Daniel (&) K. Thangavel R. Subash Chandra Boss Department of Computer Science, Periyar University, Salem 636011, India e-mail: [email protected] K. Thangavel e-mail: [email protected] R. Subash Chandra Boss e-mail: [email protected] R. Manavalan Department of Computer Application, KSR Arts and Science College, Trichengodu, India e-mail: [email protected]
G. S. S. Krishnan et al. (eds.), Computational Intelligence, Cyber Security and Computational Models, Advances in Intelligent Systems and Computing 246, DOI: 10.1007/978-81-322-1680-3_10, Springer India 2014
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gas sensor has been widely accepted and being used by researchers in this field. In fact, an array of different gas sensors is used to generate a unique signature for each gas [1]. A single sensor in the array should not be highly specific in its response but should respond to a broad range of compounds, so that different patterns are expected to be related to different odors [2]. Different methods have been suggested recently to compensate for sensor drift in experiments for gas identification [3]. Chemical sensor arrays combined with read-out electronics and a properly trained pattern recognition stage are considered to be the candidate instrument to detect and recognize odors as gas mixtures and volatiles [4]. After learning the features of the class, the SVM recognizes unknown samples as a member of a specific class. SVMs have been shown to perform especially well in multiple areas of biological analyses, especially functional class prediction from microarray sensors produced data [5]. It is not surprising to see that it may take several minutes, several hours, and several days to train neural networks in most of the applications. Unlike traditional popular implementations, for single-hidden-layer feedforward neural networks (SLFNs) with additive neurons, which is a new learning algorithm called extreme learning machine (ELM) [6]. T
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