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Работы, на которые ссылается эта работа

Работ: 36

Работа: Importance of Data Preprocessing for Accurate and Effective Prediction of Breast Cancer: Evaluation of Model Performance in Novel Data

  1. Random Forests

    Leo Breiman

    Статья2001Цитирований: 67
    ABI

  2. Cancer statistics for the year 2020: An overview

    Jacques Ferlay, Murielle Colombet, Isabelle Soerjomataram +4

    Статья2021Цитирований: 10
    ABI

  3. The WEKA data mining software

    Mark Hall, Eibe Frank, Geoffrey Holmes +3

    Статья2009Цитирований: 7
    ABI

  4. Simple and Accurate Classification Method Based on Class Association Rules Performs Well on Well-Known Datasets

    Jamolbek Mattiev, Branko Kavšek

    Глава2019Цитирований: 6
    ABI

  5. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

    Zewen Li, Fan Liu, Wenjie Yang +2

    Обзорная статья2021Цитирований: 6
    ABI

  6. Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

    Jamolbek Mattiev, Branko Kavšek

    СтатьяData Mining Algorithms and ApplicationsComputer Science and Information Systems2020Цитирований: 4
    ABI

  7. A CNN-based methodology for breast cancer diagnosis using thermal images

    Juan Zuluaga-Gómez, Zeina Al Masry, Khaled Benaggoune +2

    Статья2020Цитирований: 3
    ABI

  8. Ridge Estimators in Logistic Regression

    Saskia le Cessie, Hans C. van Houwelingen

    Статья1992Цитирований: 3
    ABI

  9. CMAC: Clustering Class Association Rules to Form a Compact and Meaningful Associative Classifier

    Jamolbek Mattiev, Branko Kavšek

    ГлаваData Mining Algorithms and ApplicationsLecture notes in computer science2020Цитирований: 3
    ABI

  10. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR

    Yeşim Eroğlu, Muhammed Yıldırım, Ahmet Çınar

    Статья2021Цитирований: 2
    ABI

  11. K*: An Instance-based Learner Using an Entropic Distance Measure

    John G. Cleary, Leonard E. Trigg

    Глава1995Цитирований: 2
    ABI

  12. Applying deep learning in digital breast tomosynthesis for automatic breast cancer detection: A review

    Jun Bai, Russell Posner, Tianyu Wang +2

    Обзорная статья2021Цитирований: 2
    ABI

  13. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms

    Bichen Zheng, Sang Won Yoon, Sarah S. Lam

    Статья2013Цитирований: 2
    ABI

  14. Instance-Based Learning Algorithms

    David W. Aha, Dennis Kibler, Marc K. Albert

    Статья1991Цитирований: 2
    ABI

  15. Bagging Predictors

    Leo Breiman

    Статья1996Цитирований: 2
    ABI

  16. The Role of Linear Discriminant Analysis for Accurate Prediction of Breast Cancer

    Egwom Onyinyechi Jessica, Mohamed Hamada, Saratu Ilu Yusuf +1

    Статья2021Цитирований: 2
    ABI

  17. Assessment of Model Accuracy in Eyes Open and Closed EEG Data: Effect of Data Pre-Processing and Validation Methods

    Jamolbek Mattiev, Jakob Sajovic, Gorazd Drevenšek +1

    СтатьяEEG and Brain-Computer InterfacesBioengineering2022Цитирований: 1
    ABI

  18. ACMKC: A Compact Associative Classification Model Using K-Modes Clustering with Rule Representations by Coverage

    Jamolbek Mattiev, Monte Davityan, Branko Kavšek

    СтатьяData Mining Algorithms and ApplicationsMathematics2023Цитирований: 1
    ABI

  19. Без названия

    ДругоеЦитирований: 1
    ABI

  20. Без названия

    ДругоеЦитирований: 1
    ABI

  21. Без названия

    ДругоеЦитирований: 1
    ABI

  22. Без названия

    ДругоеЦитирований: 1
    ABI

  23. Без названия

    ДругоеЦитирований: 1
    ABI

  24. Без названия

    ДругоеЦитирований: 1
    ABI

  25. Без названия

    ДругоеЦитирований: 1
    ABI