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This paper presents a novel approach toward the identification of odors/gases using game theory for feature selection. A previously reported tin-oxide-based sensor array capable of sensing at room temperature is chosen to extract the data. The sensor array is exposed to four different odors/gases and the response pattern obtained reveals its poor selectivity. The task of classifying the sampled data into four classes is modeled as a coalitional game in which each sensor of the array acted like a player forming coalitions with other players. A pay-off function is associated with each possible coalition of players with higher pay-offs being given to coalitions that maximize class separability of the data. Shapley value is used to quantify the contribution of each player yielding a standard pattern for each odor class. Each sample of the test vector is then matched with the standard pattern and the odor class yielding minimum Euclidean distance is assigned to the test sample. A weighting scheme for relative scaling of the test samples is also proposed. It is observed that more than 89% of the samples were identified correctly using the proposed technique thereby proving its efficacy.