Handling Sparse Data Sets by Applying Contrast Set Mining in Feature Selection

—A data set is sparse if the number of samples in a data set is not sufficient to model the data accurately. Recent research emphasized interest in applying data mining and feature selection techniques to real world problems, many of which are characterized as sparse data sets. The purpose of this research is to define new techniques for feature selection in order to improve classification accuracy and reduce the time required for feature selection on sparse data sets. The extensive comparison with benchmarking feature selection techniques conducted on 128 data sets was conducted. Results of the 1792 analysis showed that in the more than 80% of the 128 analyzed data sets contrast set mining techniques are superior to benchmarking feature selection techniques. This paper provides a study on the new methodologies that have tried to handle the sparse datasets and showed superiority in handling data sparsity. Keywords—Data characteristics, Contrast set mining, Feature selection, Neural...

• Type: Journal
• Year: 2016
• Where: JSW
• Authors: Dijana Oreski, Mario Konecki
• Keywords: JSW 2016