  This paper presents analytical techniques to improve redundancy and relevance
assessment for precise selection of features in practical multi-class raw
datasets. We propose a matrix-rank based $k$-medoids algorithm that guarantees
to output all independent medoids. The new algorithm uses matrix rank as a
robust indicator, while a traditional $k$-medoids algorithm depends on specific
datasets and how the distance between any of two features is defined. Another
advantage is that the total number of operations in the nested loops is
bounded, different from some $k$-medoids algorithms that involve random search.
Sparse regression is an efficient tool for feature relevance analysis, but its
outcome can depend on what labeled datasets are employed. A compensation method
is introduced in this paper to handle the unequality of class-occurrence in a
practical raw dataset. To assess the proposed techniques quantitatively, an
existing Industrial Control System (ICS) dataset is used to perform intrusion
detection. The numerical results generated from this case study validate the
effectiveness and necessity of the proposed analytical framework.
