Abstract
Data mining is one of the most important applications of machine learning. In machine learning algorithms, the fusion kernel principle component analysis (KPCA) and support vector machine (SVM) algorithm is used in complex data classification. To solve the problem that the fusion KPCA and SVM algorithm does not have promising classification performance, the SVM based on a feature selection algorithm for differential space fusion (DSF-FS) is proposed. First, the original data is processed to obtain differential space data by principle component analysis (PCA), and the KPCA algorithm is performed respectively on the original data and differential space data to get the differential space fusion features. Second, the ReliefF algorithm is used to get the weight of features, and the optimal feature combination is selected by a preliminary classification evaluation metric. Third, the SVM algorithm is used to classify the dimensionality reduction data. Finally, some experimental results on the five UCI datasets show that the proposed DSF-FS algorithm can not only improve the classification accuracy, but it can also reduce the computational complexity of the classification process. Moreover, the DSF-FS algorithm can be successfully applied in diabetic fundus image classification, and the encouraging results further demonstrate its strong feasibility and applicability.
Highlights
Machine learning enables machines to improve performance automatically as experience data accumulates
support vector machine (SVM) based on feature selection of differential space fusion (DSF-FS) is proposed to get better classification performance, where differential space data is used to compensate for information loss in data classification
EXPERIMENTAL RESULTS In order to evaluate the performance of the algorithm, five UCI datasets [21] were first selected for experiments
Summary
Machine learning enables machines to improve performance automatically as experience data accumulates. The algorithm can extract the principle components (PCs) with larger variance from the nonlinear data and extract the hidden classification information in the dataset Until now, it has been used in anomaly detection [4], image denoising [5], as well as other applications. It has been used in anomaly detection [4], image denoising [5], as well as other applications These two algorithms of SVM and KPCA can be effectively integrated to handle the complex data classification problem. SVM based on feature selection of differential space fusion (DSF-FS) is proposed to get better classification performance, where differential space data is used to compensate for information loss in data classification. The experiment on five UCI datasets shows that the proposed DSF-FS algorithm can effectively improve the accuracy of the classification algorithm and decrease the computation complexity.
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