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In this paper, we consider the feature selection problem in unsupervised scenarios.
Before a pattern classifier can be properly designed, it is necessary to consider the feature extraction and data reduction problems.
The similarity measures such as Euclidean, Jaccard, Cosine, Manhattan etc present in the literature only consider the count of the features but does not consider the feature distribution and the degree of commonality.
In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers.
Otherwise, we consider the feature to be deactivating.
An EE-oriented design needs to consider the feature of the specific traffic in system.
Similar(50)
Consider the features.
Consider the features you want the disposal to have.
Consider the features that you will want in your basement.
Consider the features that are important to you based on your individual needs.
Consider the features that you want in an online traffic school.
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