Signal Classification using Smooth Coefficients of Multiple wavelets

TL;DR

This paper introduces a wavelet-based transformation method for time series signal classification that enhances accuracy and efficiency by combining multiple wavelet coefficients and ensemble classifiers.

Contribution

It proposes a novel approach that selects suitable wavelets, combines their coefficients, and applies ensemble classifiers, improving classification performance over traditional methods.

Findings

Enhanced classification accuracy with the proposed wavelet combination.

Reduced dimensionality leads to faster classification.

Effective across various datasets and classifiers.

Abstract

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.