Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis

TL;DR

This comprehensive review explores online learning methods capable of handling unpredictable, haphazard input data streams, providing classifications, evaluations, code implementations, and considerations of environmental impact.

Contribution

It introduces a detailed classification and evaluation of online learning techniques for haphazard inputs, including code and carbon footprint analysis, filling a gap in existing literature.

Findings

Methods effectively model haphazard inputs

Classification of datasets for haphazard data

Evaluation metrics for imbalanced datasets

Abstract

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