Abstract:
This paper presents a novel model of a supervised machine learning approach for
classification of a dataset. The model depends on a feature selection (dimensionality
reduction) method that is based on pattern-based subspace clustering technique. Then
this clustering technique is applied to the dataset to perform the classification of the
data. This approach is a non-statistical technique that supports most of the requirements
that have been discussed recently like dimensionality reduction using multivariate
feature selection method, threshold independence and handling of missing data. The
approach tends to handle these requirements altogether which not the case in other
classification models as discussed in this paper. Another distinguishing point in this
model is its dependence on the variation of the values of relative features among
different objects. Experimental results on synthetic and real datasets show that approach
outperforms the existing methods in both efficiency and effectiveness.