Rough set based classification on electronic nose data for black tea application

Abstract

The responses generated by a gas sensor array are difficult to classify due to their inherent imprecision, uncertainty and the procedures of computational intelligence are appropriate to deal with such imperfect knowledge. In recent years, rough set theory has attracted more attention of many researchers even though it was proposed in the early 1980’s by Z. Pawlak. The rough set based analysis makes it very convenient for classification of data especially with huge volume of information, as the method is very efficient to find the op-timal subset of attributes. In this paper, the rough set based algorithm has been applied to generate representative rules using the datasets obtained from a gas sensor array in an electronic nose instrument, capable of sensing aroma of black tea samples and these rules are used to classify the black tea quality.