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IEEE Transactions on Knowledge and Data Engineering — Template for authors

Publisher: IEEE

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Guideline source

Categories	Rank	Trend in last 3 yrs
Information Systems	#11 of 329	up by 1 rank
Computational Theory and Mathematics	#5 of 133	up by 2 ranks
Computer Science Applications	#24 of 693	down by 4 ranks

Journal quality:

High

Last 4 years overview: 717 Published Papers | 9531 Citations

Indexed in: Scopus

Last updated: 06/06/2020

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General info

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

4.935

28% from 2018

Impact factor for IEEE Transactions on Knowledge and Data Engineering from 2016 - 2019
Year	Value
2019	4.935
2018	3.857
2017	2.775
2016	3.438

Graph view

13.3

11% from 2019

CiteRatio for IEEE Transactions on Knowledge and Data Engineering from 2016 - 2020
Year	Value
2020	13.3
2019	12.0
2018	9.5
2017	9.4
2016	8.8

Graph view

Insights

Impact factor of this journal has increased by 28% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has increased by 11% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.36

24% from 2019

SJR for IEEE Transactions on Knowledge and Data Engineering from 2016 - 2020
Year	Value
2020	1.36
2019	1.781
2018	1.14
2017	1.133
2016	1.325

Graph view

4.097

6% from 2019

SNIP for IEEE Transactions on Knowledge and Data Engineering from 2016 - 2020
Year	Value
2020	4.097
2019	3.858
2018	3.122
2017	3.014
2016	3.676

Graph view

Insights

SJR of this journal has decreased by 24% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has increased by 6% in last years.
This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Knowledge and Data Engineering

Guideline source: View

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IEEE

IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly. The information published in this journal is designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. We are interested in well-defined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. We welcome treatments of the role of knowledge and data in the development and use of information systems and in the simplification of software and hardware development and maintenance. Since the journal is archival, it is assumed that the ideas presented are important, have been well analyzed or empirically validated, and are of value to the knowledge and data engineering research community. Specific topics include, but are not limited to: (a) knowledge discovery and data mining, (b) data modeling and management, (c) underlying computational platforms for knowledge and data engineering tools, techniques and systems, and (d) emerging knowledge and data engineering applications. Read Less

Computer Science

Last updated on	06 Jun 2020
ISSN	1041-4347
Impact Factor	Very High - 4.412
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	IEEEtran
Citation Type	Numbered [25]
Bibliography Example	C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Journal Article • DOI: 10.1109/TKDE.2009.191 •

A Survey on Transfer Learning

Sinno Jialin Pan¹, Qiang Yang¹ •

01 Oct 2010 - IEEE Transactions on Knowledge and Data Engineering

Abstract:

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, b... A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. read more

Topics:

Semi-supervised learning (69%)69% related to the paper, Inductive transfer (68%)68% related to the paper, Multi-task learning (67%)67% related to the paper,

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18,616 Citations

Journal Article • DOI: 10.1109/TKDE.2005.99 •

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Gediminas Adomavicius¹, Alexander Tuzhilin •

01 Jun 2005 - IEEE Transactions on Knowledge and Data Engineering

Abstract:

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current r... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. read more

Topics:

Recommender system (59%)59% related to the paper, Collaborative filtering (57%)57% related to the paper, Cold start (55%)55% related to the paper,

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9,873 Citations

Journal Article • DOI: 10.1109/TKDE.2008.239 •

Learning from Imbalanced Data

Haibo He¹, E.A. Garcia¹ •

01 Sep 2009 - IEEE Transactions on Knowledge and Data Engineering

Abstract:

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing k... With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data. read more

Topics:

Active learning (58%)58% related to the paper, Knowledge extraction (54%)54% related to the paper, Raw data (53%)53% related to the paper,

6,320 Citations

Open access • Journal Article • DOI: 10.1109/TKDE.2005.66 •

Toward integrating feature selection algorithms for classification and clustering

Huan Liu¹, Lei Yu¹ •

01 Apr 2005 - IEEE Transactions on Knowledge and Data Engineering

Abstract:

This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, a... This paper introduces concepts and algorithms of feature selection, surveys existing feature selection algorithms for classification and clustering, groups and compares different algorithms with a categorizing framework based on search strategies, evaluation criteria, and data mining tasks, reveals unattempted combinations, and provides guidelines in selecting feature selection algorithms. With the categorizing framework, we continue our efforts toward-building an integrated system for intelligent feature selection. A unifying platform is proposed as an intermediate step. An illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms. An added advantage of doing so is to help a user employ a suitable algorithm without knowing details of each algorithm. Some real-world applications are included to demonstrate the use of feature selection in data mining. We conclude this work by identifying trends and challenges of feature selection research and development. read more

Topics:

Feature selection (64%)64% related to the paper, Minimum redundancy feature selection (62%)62% related to the paper, Feature (computer vision) (60%)60% related to the paper,

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2,605 Citations

Journal Article • DOI: 10.1109/TKDE.2013.39 •

A Review On Multi-Label Learning Algorithms

Min-Ling Zhang¹, Zhi-Hua Zhou² •

01 Aug 2014 - IEEE Transactions on Knowledge and Data Engineering

Abstract:

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area w... Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes. read more

Topics:

Algorithmic learning theory (64%)64% related to the paper, Learning sciences (63%)63% related to the paper, Inductive transfer (61%)61% related to the paper,

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2,495 Citations

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Frequently asked questions

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12. Is IEEE Transactions on Knowledge and Data Engineering's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Knowledge and Data Engineering?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Knowledge and Data Engineering. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Knowledge and Data Engineering?

The 5 most common citation types in order of usage for IEEE Transactions on Knowledge and Data Engineering are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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IEEE Transactions on Knowledge and Data Engineering — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

4.935

13.3

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

1.36

4.097

Insights

Insights

IEEE Transactions on Knowledge and Data Engineering

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

Freedom from formatting guidelines

Easy support from all your favorite tools

Frequently asked questions

Fast and reliable, built for complaince.

No word template required

Verifed journal formats

Trusted by academicians

Fast and reliable,
built for complaince.