A New Binary Classifier: Clustering-Launched Classification
One of the powerful classifiers is Support Vector Machine (SVM), which has been successfully applied to many fields. Despite its remarkable achievement, SVM is time-consuming in many situations where the data distribution is unknown, causing it to spend much time on selecting a suitable kernel and setting parameters. Previous studies proposed understanding the data distribution before classification would assist the classification. In this paper, we exquisitely combined with clustering and classification to develop a novel classifier, Clustering-Launched Classification (CLC), which only needs one parameter. CLC employs clustering to group data to characterize the features of the data and then adopts the one-against-the-rest and nearest-neighbor to find the support vectors. In our experiments, CLC is compared with two well-known SVM tools: LIBSVM and mySVM. The accuracy of CLC is comparable to LIBSVM and mySVM. Furthermore, CLC is insensitive to parameter, while the SVM is sensitive, showing CLC is easier to use.
KeywordsSupport Vector Machine Support Vector Intrusion Detection Intrusion Detection System Binary Classifier
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