An adaptive medical image registration using hybridization of teaching learning-based optimization with affine and speeded up robust features with projective transformation

Arora, Paluck; Mehta, Rajesh; Ahuja, Rohit

doi:10.1007/s10586-023-03974-3

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…Another advantage of HAT over CVFDT is that it offers guaranteed performance. Buntine (2020) suggested option trees as an extension to normal decision trees, that were subsequently investigated by Kohavi and Kunz (1997) and Arora et al (2023). Traditional decision trees only have one way to get from the root to the leaf node, which is where predictions are generated.…”

Section: Concept Drift Detection Techniquesmentioning

confidence: 99%

A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques

Arora,

Rani,

Saxena

2024

WIREs Data Min & Knowl

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Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real‐time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts.This article is categorized under: Algorithmic Development > Ensemble Methods Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Big Data Mining

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Section: Concept Drift Detection Techniquesmentioning

confidence: 99%