Robust Feature Sets for Implementation of Classification Machines
Keywords:Classifying Machine, Discrete sine transform, Statistical estimators, Hidden Markov Models, H-statistic, F-statistic
Classification Machines have evolved over a lot during recent times, in the field of engineering and sciences. Various classification schemes have been developed, taking into account, the aspect that can be optimized to give maximum system performance. The feature set in a classifier system is very significant, since it determines the efficiency and performance of the machine. Three powerful feature sets possessing robust classifying capabilities are discussed in this paper. Cepstral coefficient analysis based Kruskal-Wallis H statistic, F-test statistic and Discrete Sine Transform based features are found to be very effective for detection and classification of signals. Simulation results for typical data set are also presented in this paper. Statistical estimators, Neural Network and Hidden Markov Model based classifiers, along with various deep learning algorithms can be incorporated along with these feature sets to implement an efficient classifying machine. Typical results based on these feature sets are also presented for different signal sources.
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