Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Hashmani, Manzoor Ahmed; Jameel, Syed Muslim; Rehman, Mobashar

doi:10.21307/ijssis-2020-029

Cited by 6 publications

(2 citation statements)

References 68 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[21] According to previous researches on the typology of concept drift, there are mainly four different types, including sudden drift, incremental drift, gradual drift, and reoccurring drift. [22] Examples are shown in Figure 3.…”

Section: Concept Drift In Industrial Processmentioning

confidence: 99%

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

Zhang

Ding

Sun

et al. 2023

steel research int.

View full text Add to dashboard Cite

In the hot strip process, the prediction of strip shape is a key factor to improve the quality of products. However, strip‐shape prediction models based on traditional machine learning algorithms with relatively simple network structures often rely on data preprocessing and feature selection. Herein, five strip‐shape prediction models are proposed by combining sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), extreme learning machine (ELM), and deep multilayer extreme learning machine (DELM). The prediction experiments show that the SSA‐DELM model has highest accuracy, and the determination coefficient (R2) of crown prediction and flatness prediction of SSA‐DELM model is 0.971 and 0.979. 100% of the samples have a prediction error of less than ±7 μm and ±7 IU. Furthermore, to solve the problem of concept drift affecting prediction accuracy in industrial processes, online optimization method is proposed. Especially, online sequential deep ELM model (OS‐DELM) optimized by SSA and guided by double window drift detection is proposed to fill the gap. Compared with other online optimization methods, the update time with this method can be reduced by up to 83.05% averagely, which is more suitable for online strip‐shape prediction in hot rolling process.

show abstract

Section: Concept Drift In Industrial Processmentioning

confidence: 99%

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

Zhang

Ding

Sun

et al. 2023

steel research int.

View full text Add to dashboard Cite

show abstract

“…Hence the resilience of our model against evolution needs to be looked at. Although recent works are seen to propose solutions to tackle concept drift [37], this work does not factor in such measures, therefore we entreat a constant update upon deployment.…”

Section: Limitationsmentioning

confidence: 99%

NLP Technique for Malware Detection Using 1D CNN Fusion Model

Yeboah

Musah

2022

Security and Communication Networks

View full text Add to dashboard Cite

With the record of the highest market share of mobile operating systems, the Android operating system has become a prime target for cyber perpetrators as malicious applications are leveraged as attack vectors to exploit Android systems. Machine learning detection solutions that have become a resort mostly rely on handcrafted features, a process deemed to be laborious and time-consuming. In this article, we employ a deep learning-based model consisting of 1-dimensional convolutional neural network (1D CNN) to automate the detection of Android malware. Our choice of 1D CNN was motivated by the computational advantage of 1D convolution operations over 2D CNN. The proposed model automatically extracts features from semantically embedded n-grams of raw static operation code (opcodes) sequences to determine the maliciousness of a binary file. Predictions of the 1D CNN model trained on multiple feature sets of n-gram opcode sequences are combined using a weighted average ensemble. Optimal prediction weights were obtained using a grid search on values in the range 0 to 1. With an Android dataset comprising 4951 malware and 2477 benign samples, our model yielded a positive predictive value of 98% and sensitivity of 97% using a weight parity of 0.5 for ensemble unigram and bigram opcode sequences.

show abstract

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

View full text Add to dashboard Cite

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

show abstract

Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Cited by 6 publications

References 68 publications

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

Prediction and Online Optimization of Strip Shape in Hot Strip Rolling Process Using Sparrow Search Algorithm‐Online Sequential‐Deep Multilayer Extreme Learning Machine Algorithm

NLP Technique for Malware Detection Using 1D CNN Fusion Model

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

Contact Info

Product

Resources

About