Computationally Efficient Weighted Binary Decision Codes for Gas Identification With Array of Gas Sensors

Motivated by biological olfactory coding principles, rank-code-based classifiers have recently been proposed to facilitate integration of hardware-friendly gas identification platforms with an array of gas sensors. These classifiers operate on a simple principle of generating rank-codes by ranking the gas sensors' features instead of treating them as a multi-dimensional vector as in computation-intensive state-of-the-art gas classifiers. However, the performance of the rank-code-based classifiers is limited when distinguishing information about all the target gases is not found in the ranks of the gas sensors' features, but in their values. In this paper, we introduce a computationally efficient alternative solution to overcome this limitation. In this solution, an original multi-gas classification problem is decomposed into pairwise classifiers, and the gas is then predicted with the weighted binary decision codes in each of these classifiers, where each element of the code is generated by exploiting features individually. The weighted binary decision codes are formed by first using the nearest centroid approach, which exploits the mean value of each gas sensor's feature to generate binary decision codes, and then, a simple approach is used to assign a weight to each element of the code, depending upon its capability to discriminate the gases in each pairwise classifier. The added advantage of this classification approach is that two computationally efficient metrics are introduced to access the classifiers' applicability to the given data set and certainty about the prediction of any test sample. A classification performance of 97.87% is achieved with this approach on an extensive data set of ten gases experimentally obtained with Figaro series gas sensors, and this is increased to 100% by rejecting 3.37% of samples for which certainty about their predictions is below a 25% confidence level.