As an important part of intrusion detection, feature selection plays a significant role in improving the performance detection. Krill herd (KH) algorithm is efficient swarm intelligence with excellent data mining. To solve problem low efficiency and high false positive rate detection caused by increasing high-dimensional data, improved krill based on linear nearest neighbor lasso step (LNNLS-KH...

Feature selection is one of key issues related with data pre-processing of classification task in a data mining process. Although many efforts have been done to improve typical feature selection algorithms (FSAs), such as filter methods and wrapper methods, it is hard for just one FSA to manage its performances to various datasets. To above problems, we propose another way to support feature se...

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Latent Semantic Indexing (LSI) is an effective method to extract features that captures underlying latent semantic structure in the word usage across documents. However, subspace selected by this method may not be the most appropriate one to classify documents, since it orders extracted features according to their variances, not the classification power. We propose to apply feature ordering met...

Abstract One challenge of studying cloud workload traces is the lack available users’ identities. Therefore, clustering methods were used to address this through extracting these identities from traces. For better extraction, it beneficial select attributes (columns in traces) for by using feature selection methods. However, use general requires details that are not (e.g. predefined number clus...