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    A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated withk-Fold Cross-Validation
    (Springer Heidelberg, 2021) Inan, Onur; Uzer, Mustafa Serter
    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 modifiedk-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 thek-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.