A unified framework for Volatile Organic Compound classification and regression

Motivated by the insect olfactory system, which resolves both the identity and the quantity of a nectar in parallel based on the same sensory cue, we address the problem of Volatile Organic Compound (VOC) classification and regression in a unified setting. We derive a maximum margin formulation for minimizing the empirical regression error and the classification error jointly, and then call the sequential minimal optimization procedure for solution. The solution yields a pool of support vectors that achieves both tasks almost equally accurately as individual performances of a support vector machine classifier and a support vector regressor designed independently. We investigate empirically the advantages and inconveniences of handling these two problems under a single formulation for odor identification and quantification. We demonstrate the method on an extensive dataset acquired by an array metal-oxide sensors for five VOC identities and a wide range of concentrations.