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1.3.1 Feature Selection

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Feature Selection selects the most relevant attributes by identifying the most pertinent characteristics and eliminating redundant information. A small size feature vector is used to reduce computational complexity, basic for online individual acknowledgment. Determination of effective features also helps increase precision [11]. Traditionally, large dimensions of feature vectors can be reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

The importance of a feature set S, for class c, is characterized by the normal estimation of all common data values between the individual feature fi and class c as follows:


The repetition of all features in set S is the normal estimation of all common data values between feature fi and feature fj:


Deep Learning Approaches to Cloud Security

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