Signal Classification using Smooth Coefficients of Multiple wavelets
This work addresses the need for efficient and accurate signal classification in practical applications, though it appears incremental as it builds on existing wavelet and ensemble techniques.
The paper tackled the problem of classifying time series signals with reduced attributes by proposing a method that selects multiple wavelets for transformation and combines their outputs for ensemble classification, resulting in improved effectiveness compared to using raw data or single wavelet transforms.
Classification of time series signals has become an important construct and has many practical applications. With existing classifiers we may be able to accurately classify signals, however that accuracy may decline if using a reduced number of attributes. Transforming the data then undertaking reduction in dimensionality may improve the quality of the data analysis, decrease 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 transforms to construct a dataset to then apply ensemble classifiers to. We demonstrate this on different data sets, across different classifiers and use differing 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.