ADVANCED FEATURE SELECTION FOR HUMAN PHYSIOLOGICAL STATE PREDICTION USING ERFE
DOI:
https://doi.org/10.29121/shodhkosh.v5.i3.2024.2755Keywords:
Classification, ERFE, Feature Selection, Human Activity Recognition, LASSO, Random Forest, RFE', Performance EvaluationAbstract [English]
Human Activity Recognition (HAR) is becoming increasingly important in healthcare as the volume of sensor data grows. Medical practitioners often struggle to quickly and accurately interpret this data to recognize physiological states. Machine learning and feature selection methods can help address this challenge by pinpointing essential features, thereby reducing processing time and enhancing accuracy. This paper introduces an Enhanced Recursive Feature Elimination (ERFE) method for refining feature selection in HAR prediction. Experimental results demonstrate that the ERFE method achieves an 88% classification accuracy, surpassing traditional approaches like LASSO, Random Forest (RF), and standard Recursive Feature Elimination (RFE).
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