  In this paper, a novel feature selection approach for supervised interval
valued features is proposed. The proposed approach takes care of selecting the
class specific features through interval K-Means clustering. The kernel of
K-Means clustering algorithm is modified to adapt interval valued data. During
training, a set of samples corresponding to a class is fed into the interval
K-Means clustering algorithm, which clusters features into K distinct clusters.
Hence, there are K number of features corresponding to each class.
Subsequently, corresponding to each class, the cluster representatives are
chosen. This procedure is repeated for all the samples of remaining classes.
During testing the feature indices correspond to each class are used for
validating the given dataset through classification using suitable symbolic
classifiers. For experimentation, four standard supervised interval datasets
are used. The results show the superiority of the proposed model when compared
with the other existing state-of-the-art feature selection methods.
