Interpretable Signal Analysis with Knockoffs Enhances Classification of Bacterial Raman Spectra

EW sensor technologies have contributed to the advent of "big data" in biomedicine, of which signal data are for example, saliency methods help visualize the activation of an important modality. From one-dimensional electrocardiography individual input features [5], while attribution methods like and electroencephalography signals from the heart and LIME [6] and SHAP [7] quantify the impact of each feature brain, to two-dimensional tissue images of tumor histology, to on the output predictions. However, these post hoc techniques three-dimensional magnetic resonance images, these consist are inadequate for developing simpler models. of sequential measures of an observable along one or more With regard to relevancy, studies report that people favor independent axes such as time, distance, or frequency. Signal explanations that are short, contrast instances with different data differ from structured forms of data in that the meaning outcomes, and highlight abnormal causes [8]. In other words, of each independent variable is not as distinctively and intuitively we seek to understand which features are important, and definable. Informative features must be extracted from how these affect the outcome. Data scientists often pursue these raw data using signal processing and machine learning these goals through feature selection, in addition to feature (ML) techniques before useful patterns can be detected and extraction, to ensure that their conclusions are based on leveraged to make predictions.