STM Journals Peer-reviewed research from Consortium e-Learning Network

Current Trends in Signal Processing Review Article

A Study on Feature Subset Selection in Feature Streams of Dynamic Data

  1. Priyadarshini Department of Computer Engineering MET’s Institute of Engineering Bhujbal Knowledge City (BKC),Nashik
  2. Shirish S. Sane Department of Computer Engineering, Gokhale’s Education Society R H Sapat College of Engineering, Nashik

Abstract

As the use of real-time data with high dimensions continues to expand across various domains, selecting important features from the dataset is a key step to improve the predictive accuracy and time taken to build a machine learning model. In datasets where not all features are available at the same time and we are unaware of the total number of features, and features arrive at different time stamps, for example, in real-time patient monitoring in a hospital’s intensive care unit (ICU), feature selection becomes even more challenging. In an ICU, patients are continuously monitored using various medical instruments with sensors for monitoring heart rate, blood pressure, oxygen saturation, and electrocardiograms (ECG), etc.These devices stream data at different intervals, often providing updates asynchronously.

Keywords

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