Embedded methods, feature selection

An efficient method of feature selection and hence sensor optimization for an e-nose system is described in this work with the help of a problem of black tea quality prediction. This work shows that a feature set comprising of few features from the highest ranking of a feature selection algorithm will not necessarily produce the best classification performance. Since the performance of a classifier depends on the choice of the parameter also. Therefore, the feature and parameter of a classifier should be selected simultaneously to obtain the optimum performance. In our future work we shall look after this issue by using wrapper or embedded method of feature selection in this application. 

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