Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme

Data Compactness Versus Prediction Performance: Achieving Both by Pruning Redundant Samples With Dominant Patterns and Hamming Distance Based Sampling Scheme

Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. However, most real-world datasets are noisy, imbalanced, and contain redundant samples, prompting the need to address these problems before the datase...

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Bibliographic Details
Main Authors: Abdul Majeed, Seong Oun Hwang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Data diversity
machine learning
sample pruning
training data
predictive performance
Online Access:https://ieeexplore.ieee.org/document/10982066/
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