Hybridization of Rough Setsand Multi-ObjectiveEvolutionary Algorithms forClassificatory SignalDecomposition

Abstract

Classification of sampled continuous signals into one of a finite number of predefined classes is possible when some distance measure between the signals in the dataset is introduced. However, it is often difficult to come up with a “temporal” distance measure that is both accurate and efficient computationally. Thus in the problem of signal classification, extracting particular features that distinguish one process from another is crucial. Extraction of such features can take the form of a decomposition technique, such as Principal Component Analysis (PCA) or Independent Component Analysis (ICA). Both these algorithms have proven to be useful in signal classification. However, their main flaw lies in the fact that nowhere during the process of decomposition is the classificatory aptitude of the components taken into consideration. Thus the ability to differentiate between classes, based on the decomposition, is not assured. Classificatory decomposition (CD) is a general term that describes attempts to improve the effectiveness of signal decomposition techniques by providing them with “classification-awareness.” We propose a hybridization of multi-objective evolutionary algorithms (MOEA) and rough sets (RS) to perform the task of decomposition in the light of the underlying classification problem itself.