Signal classification using smooth coefficients of multiple wavelets to achieve high accuracy from compressed representation of signal

Classification of time series signals has become an important construct and has many practical applications. With existing classifiers, we may be able to classify signals accurately; however, that accuracy may decline if using a reduced number of attributes. Transforming the data and then undertaking a dimensionality reduction may improve the quality of the data analysis, decrease the time required for classification and simplify models. We propose an approach, which chooses suitable wavelets to transform the data, then combines the output from these transformations to construct a dataset by applying ensemble classifiers. We demonstrate this on different data sets across different classifiers and use different evaluation methods. Our experimental results demonstrate the effectiveness of the proposed technique, compared to the approaches that use either raw signal data or a single wavelet transform.