Imbalanced data classification is a common issue in data mining where the classifiers are skewed towards the larger data class. Classification of high-dimensional skewed (imbalanced) data is of great interest to decisionmakers as it is more difficult to. The dimension reduction method, a process in which variables are reduced, allows high dimensional datasets to be interpreted more easily with a certain loss. This study, a method combining SMOTE oversampling with principal component analysis is proposed to solve the imbalance problem in high dimensional data. Three classification algorithms consisting of Logistic Regression, K-Nearest Neighbor, Decision Tree methods and two separate datasets were utilized to evaluate the suggested method's efficacy and determine the classifiers' performance. Respectively, raw datasets, converted datasets by PCA, SMOTE and SMOTE+PCA (SMOTE and PCA) methods, were analyzed with the given algorithms. Analyzes were made using WEKA. Analysis results suggest that almost all classification algorithms improve their classification performance using PCA, SOMTE, and SMOTE+PCA methods. However, the SMOTE method gave more efficient results than PCA and PCA+SMOTE methods for data rebalancing. Experimental results also suggest that K-Nearest Neighbor classifier provided higher classification performance compared to other algorithms.
In this article, two machine learning methods such as classification and clustering are used for decision tree (DT), artificial neural network (ANN), and K-nearest neighbors algorithms. The datasets were used to evaluate the effectiveness of the clustering method and the data mining tool. Weather data were used to compare algorithms and methods in the study. This study showed that the best model was DT according to accuracy and precision measures but the best model according to F-measure and receiver operating characteristic curve area measures was ANN. Waikato Environment for Knowledge Analysis, a data mining tool, is utilized in this paper to carry out the clustering.
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