A Method of Classification Performance Improvement Via a Strategy of Clustering-Based Data Elimination Integrated with k-Fold Cross-Validation

摘要

Non-system errors that occur during data entry or data collection create noisy data that reduce the success of classificationsystems. To eliminate this data, a classification system with a new data reduction method consisting of a modified k-meansalgorithm using relief algorithm coefficients named MKMA-RAC was developed. The main theme of this article is theelimination 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 classifierswithMKMA-RAC-based data reduction for every fold. The datareduction 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 bothfrom 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 SVMclassifier were 96.88%, 74.56%, 87.24%, and 90.00%, classification successes of the proposed system with LDA classifierwere 94.91%, 69.05%, 82.38%, and 88.52%, classification successes of the proposed system with decision tree classifier were96.25%, 77.73%, 88.77% and 89.63%, respectively. The test results have shown that the proposed system generally achievedhigher classification performance than other literature results. Therefore, the performance is very encouraging for patternrecognition applications.