Hierarchical classification of data streams: a systematic literature review

Tieppo, Eduardo; Santos, Roger R. dos; Barddal, Jean Paul; Nievola, Júlio César

doi:10.1007/s10462-021-10087-z

Cited by 9 publications

(6 citation statements)

References 77 publications

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“…The primary purpose of data synthesis is to combine evidence collected from research selected to answer research questions. Although one piece of evidence may have limited proof strength, the cumulative effect of some pieces of evidence can significantly enhance the persuasiveness of an argument [16], [17]. Various methodologies are used to combine data collected in relation to research questions (RQ).…”

Section: F Data Synthesismentioning

confidence: 99%

A Systematic Literature Review: Recursive Feature Elimination Algorithms

Priyatno,

Widiyaningtyas

2024

jitk

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Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.

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Section: F Data Synthesismentioning

confidence: 99%

A Systematic Literature Review: Recursive Feature Elimination Algorithms

Priyatno,

Widiyaningtyas

2024

jitk

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“…Competence indicators with similar meanings can be summarised and classified to enhance clarity and operability, and reduce the number of redundant indicators (Tieppo et al, 2022). For instance, information technology, AR technology, standards-based classrooms, basic tools, complex tools and technology empowerment can be collectively categorised under tool usage (Aljawarneh, 2020).…”

Section: Technological Knowledge Competency Indicatorsmentioning

confidence: 99%

Exploring Informatizaon Instructional Core Competence of Higher Vocational College Teacher for Professional Development

Xu,

Chen,

Zhang

2024

JESR

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In the dynamic landscape of educational information technology, the proficiency of vocational college teachers in this domain has garnered significant attention due to its rapid evolution. The purpose of this study is to comprehensively explore the foundational aspects of vocational college teachers' competence in information technology instruction. The primary objective is to augment these teachers’ capabilities and foster their professional growth in the realm of information technology teaching. The six pivotal elements rooted in TPACK (Technological Pedagogical Content Knowledge) are analysed using a literature-based approach. These elements encompass technological knowledge, content knowledge, pedagogical knowledge, technological pedagogical knowledge, pedagogical content knowledge, and technological pedagogical content knowledge. The findings illustrate the distinctive contribution of each element to the information technology teaching competence of vocational college teachers, thereby facilitating their holistic professional development. The nuanced enhancement of these competency indicators equips teachers to adeptly navigate the demands of information technology instruction, cultivates their professional prowess, and enables them to deliver the highest-quality educational services. Received: 2 January 2024 / Accepted: 29 February 2024 / Published: 5 March 2024

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“…Examples include text categorization, protein function prediction, musical genre classification, speech classification, computer vision, COVID-19 identification, marine benthic biota, satellite spectral images, and forensics [ 29 , 37 , 43 , 49 , 53 , 61 ]. We refer the reader to the recent survey [ 52 ] and references therein for more details.…”

Section: Introductionmentioning

confidence: 99%

Fooling the Big Picture in Classification Tasks

Alkhouri

Atia

Mikhael

2022

Circuits Syst Signal Process

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Minimally perturbed adversarial examples were shown to drastically reduce the performance of one-stage classifiers while being imperceptible. This paper investigates the susceptibility of hierarchical classifiers, which use fine and coarse level output categories, to adversarial attacks. We formulate a program that encodes minimax constraints to induce misclassification of the coarse class of a hierarchical classifier (e.g., changing the prediction of a ‘monkey’ to a ‘vehicle’ instead of some ‘animal’). Subsequently, we develop solutions based on convex relaxations of said program. An algorithm is obtained using the alternating direction method of multipliers with competitive performance in comparison with state-of-the-art solvers. We show the ability of our approach to fool the coarse classification through a set of measures such as the relative loss in coarse classification accuracy and imperceptibility factors. In comparison with perturbations generated for one-stage classifiers, we show that fooling a classifier about the ‘big picture’ requires higher perturbation levels which results in lower imperceptibility. We also examine the impact of different label groupings on the performance of the proposed attacks. Supplementary Information The online version contains supplementary material available at 10.1007/s00034-022-02226-w.

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Hierarchical classification of data streams: a systematic literature review

Cited by 9 publications

References 77 publications

A Systematic Literature Review: Recursive Feature Elimination Algorithms

A Systematic Literature Review: Recursive Feature Elimination Algorithms

Exploring Informatizaon Instructional Core Competence of Higher Vocational College Teacher for Professional Development

Fooling the Big Picture in Classification Tasks

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