Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
Arabian Journal for Science and Engineering · Şubat 2021
YÖKSİS Kayıtları
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
Arabian Journal for Science and Engineering · 2021 SCI-Expanded
Doç. Dr. MUSTAFA SERTER UZER →
A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
Arabian Journal for Science and Engineering · 2021 SCI-Expanded
Dr. Öğr. Üyesi ONUR İNAN →
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A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation
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Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm
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Makale Bilgileri
ISSN2193567X
Yayın TarihiŞubat 2021
Cilt / Sayfa46 · 1199-1212
Scopus ID2-s2.0-85091735926
Özet
Non-system errors that occur during data entry or data collection create noisy data that reduce the success of classification systems. To eliminate this data, a classification system with a new data reduction method consisting of a modified k-means algorithm using relief algorithm coefficients named MKMA-RAC was developed. The main theme of this article is the elimination of noisy data and its consistent application to the classification system using the k-fold cross-validation method. By means of the developed system, the training data became free from noisy data by integrating the support vector machine, linear discriminant analysis (LDA) and decision tree classifiers with MKMA-RAC-based data reduction for every fold. The data reduction process was not applied for the test data. Datasets used in the proposed method were the Hepatitis, Liver Disorders, SPECT images and Statlog (Heart) dataset taken from the UCI database. Classification performance values obtained both from the proposed method and without the proposed method with tenfold CV were given for these datasets. For Hepatitis, Liver Disorders, SPECT images and Statlog (Heart) datasets, and classification successes of the proposed system with SVM classifier were 96.88%, 74.56%, 87.24%, and 90.00%, classification successes of the proposed system with LDA classifier were 94.91%, 69.05%, 82.38%, and 88.52%, classification successes of the proposed system with decision tree classifier were 96.25%, 77.73%, 88.77% and 89.63%, respectively. The test results have shown that the proposed system generally achieved higher classification performance than other literature results. Therefore, the performance is very encouraging for pattern recognition applications.
Yazarlar (2)
1
Onur Inan
2
Mustafa Serter Uzer
ORCID: 0000-0002-8829-5987
Anahtar Kelimeler
Clustering-based data elimination
Medical dataset classification
Relief
Kurumlar
Necmettin Erbakan Üniversitesi
Meram Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Arabian Journal for Science and Engineering
Q1
SJR Skoru0,545
H-Index89
ÜlkeGermany
Multidisciplinary (Q1)
Metrikler
17
Atıf
2
Yazar
3
Anahtar Kelime