Electronic Nose Sensor Array Optimization Using Rough Set Theory

The most important component of an electronic nose instrument is the sensor array and its classification accuracy depends significantly upon the choice of the sensors in the array. In many applications of electronic nose, a few sensors are sometimes redundant and only a subset of the sensor array contributes to the decision. Thus, the number of sensors used in the electronic nose may be minimized for a particular application without affecting the classification accuracy. In many cases, the sensor array produces even an imprecise, redundant and inconsistent dataset. Rough set theory (RST) is a mathematical tool capable of selecting the most relevant and non‐redundant features from such datasets. Compared to the other techniques of sensor array optimization, the unique feature of the RST based method is that it optimizes the sensor array while classifying the data. There is no need to execute a special procedure for optimization. This feature makes this method very useful while introducing the electronic no...