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Feature selection is a process which chooses a subset from the original feature set according to some rules. The selected feature retains original physical meaning and provides a better understanding for the data and learning process. However, few modern feature selection approaches take the advantage of features' context information. Based on this analysis, we propose a novel feature selection method...
Feature selection is an important issue in machine learning. Rough set theory is one of the important methods for feature selection. In rough set theory, feature selection has already been separately studied in algebra view and information view. Unfortunately, the previously proposed methods based on information entropy for feature selection only focus on the discrete datasets. However, how to effectively...
Inspired by complementary strategies, a hybrid supervised artificial immune classifier is put forward, which is on the basis of the clonal selection principle, and combined with the fuzzy c-means clustering (FCM) algorithm and information entropy theory. The new approach uses a weighted Euclidean distance based dissimilarity measure during all affinity evaluations. With the help of FCM clustering,...
This work proposes a compositional approach to hand posture recognition, using sparse features. The hand posture is decomposed into relevant compositions which are learned for each hand posture class without supervision; no hand segmentations or localization during training is needed. To learn relevant composition prototypes, an entropy range maximization loop was introduced, by performing k-means...
Feature extraction (FE) methods have been proved to be very effective for dimension reduction, but the features attained are meaningless. In order to exploit the effectiveness of FE methods to support feature selection (FS), this paper proposed a new FS approach for clustering based on principal component analysis (PCA) called PS. It first uses PCA to transform the data from original feature space...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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