IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v22y2020i5d10.1007_s10796-020-10052-1.html
   My bibliography  Save this article

Heuristic Acquisition for Data Science

Author

Listed:
  • Lydia Bouzar-Benlabiod

    (Ecole Nationale Supérieure d’Informatique (ESI, Algeria))

  • Stuart H. Rubin

    (Naval Information Warfare Center (NIWC) Pacific, Intelligent Sensing Branch)

Abstract

No abstract is available for this item.

Suggested Citation

  • Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
    • Handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10052-1
    DOI: 10.1007/s10796-020-10052-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-020-10052-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-020-10052-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Justin M. Johnson & Taghi M. Khoshgoftaar, 2020. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 22(5), pages 1113-1131, October.
    2. Justin M. Johnson & Taghi M. Khoshgoftaar, 0. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    3. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 2020. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 22(5), pages 1053-1066, October.
    4. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    5. Narges Manouchehri & Hieu Nguyen & Pantea Koochemeshkian & Nizar Bouguila & Wentao Fan, 2020. "Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions," Information Systems Frontiers, Springer, vol. 22(5), pages 1085-1093, October.
    6. Qianwen Xu & Victor Chang & Ching-Hsien Hsu, 2020. "Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study," Information Systems Frontiers, Springer, vol. 22(5), pages 1021-1037, October.
    7. Sabin Kafle & Nisansa Silva & Dejing Dou, 2020. "An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering," Information Systems Frontiers, Springer, vol. 22(5), pages 1095-1111, October.
    8. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 2020. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 22(5), pages 1009-1019, October.
    9. Mohammed Kuko & Mohammad Pourhomayoun, 2020. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 22(5), pages 1039-1051, October.
    10. Mohammed Kuko & Mohammad Pourhomayoun, 0. "Single and Clustered Cervical Cell Classification with Ensemble and Deep Learning Methods," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    11. Haiman Tian & Shu-Ching Chen & Mei-Ling Shyu, 0. "Evolutionary Programming Based Deep Learning Feature Selection and Network Construction for Visual Data Classification," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    12. Narges Manouchehri & Hieu Nguyen & Pantea Koochemeshkian & Nizar Bouguila & Wentao Fan, 0. "Online Variational Learning of Dirichlet Process Mixtures of Scaled Dirichlet Distributions," Information Systems Frontiers, Springer, vol. 0, pages 1-9.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
    2. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.
    3. R. Elakkiya & Pandi Vijayakumar & Marimuthu Karuppiah, 2021. "COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking," Information Systems Frontiers, Springer, vol. 23(6), pages 1369-1383, December.
    4. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
    5. Harleen Kaur & Shafqat Ul Ahsaan & Bhavya Alankar & Victor Chang, 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets," Information Systems Frontiers, Springer, vol. 23(6), pages 1417-1429, December.
    6. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    7. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Haitham Abdulmohsin Afan & Ayman Yafouz & Ahmed H. Birima & Ali Najah Ahmed & Ozgur Kisi & Barkha Chaplot & Ahmed El-Shafie, 2022. "Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1527-1545, June.
    9. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.
    10. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    11. Peng Xie, 2022. "The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market," Information Systems Frontiers, Springer, vol. 24(4), pages 1287-1303, August.
    12. Forsberg, Charles, 2023. "Low-cost crushed-rock heat storage with oil or salt heat transfer," Applied Energy, Elsevier, vol. 335(C).
    13. Wen Chen, 2023. "Digital economy development, corporate social responsibility and low‐carbon innovation," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 30(4), pages 1664-1679, July.
    14. Ajaya K. Swain & Valeria R. Garza, 2023. "Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution," Information Systems Frontiers, Springer, vol. 25(2), pages 819-834, April.
    15. Haixia Sun & Shujuan Zhang & Rui Ren & Liyang Su, 2022. "Maturity Classification of “Hupingzao” Jujubes with an Imbalanced Dataset Based on Improved MobileNet V2," Agriculture, MDPI, vol. 12(9), pages 1-16, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10052-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.