Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis
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Abstract (arXiv)
The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly foregoing such an assumption. We discuss, classify, evaluate, and compare the methodologies that are adept at modeling haphazard inputs, additionally providing the corresponding code implementations and their carbon footprint. Moreover, we classify the datasets related to the field of haphazard inputs and introduce evaluation metrics specifically designed for datasets exhibiting imbalance. The code of each methodology can be found at https://github.com/Rohit102497/HaphazardInputsReview
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 95 references that are in the map. First posted on arXiv 7 Apr 2024.
Builds on
20 of 95 references.
References · 20
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8 more
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.