# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 7 Conclusions  
Disccretisation is often considered to be a part of initial preprocessing of the input data, thereby leading to data cleaning and possibly some reduction in dimensionality. In standard approaches, supervised methods are widely preferred, and the same type of transformation is applied to all available variables.  
This paper presents research in which the methodology for data processing was proposed, thereby employing a combination of supervised with unsupervised discretisation algorithms. The former find intervals to represent attribute values by evaluating with some measure the possible placement of borders between these intervals. In a top-down approach, it means starting with assigning a single interval to represent the entire attribute domain and then looking to some stopping criterion. When this criterion is satisfied, the processing stops, and, as a consequence, some variables can remain represented in a discrete domain by these single bins. It makes them irrelevant to classification, and the information they provide in the continuous domain becomes inaccessible or lost in translation. To avoid that, in the proposed methodology, such special 1-bin variables were subjected to additional transformations with unsupervised methods. The extensive experiments, executed in the stylometric domain on the binary authorship attribution problems, showed the merits of such two-level discretisation, as the deeper processing of some variables led to many cases of improved performance of the group of considered classifiers.  
The future research will be dedicated to application of the methodology to other application domains to evaluate the influence of data characteristics on the whole procedure. Other unsupervised discretisation algorithms will also be tested, for example, equal frequency binning with weights. Another path to tread is to establish some pointers for selection of a specific combination of discretisation methods that would be expected to offer improved performance.  
**Author Contributions:** Conceptualization, U.S.; methodology, U.S., B.Z., and G.B; software, G.B.; validation, U.S., B.Z., and G.B.; formal analysis, U.S. and B.Z.; data curation, U.S. and G.B.; writing--original draft preparation, U.S. and B.Z.; writing--review and editing, U.S. and B.Z.; visualization, U.S. and B.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Texts exploited in the experiments are available for online reading and download thanks to Project Gutenberg (www.gutenberg.org, accessed on 8 May 2024). The prepared datasets are available upon request.  
**Acknowledgments:** The research works presented in the paper were performed within the statutory project of the Department of Computer Graphics, Vision, and Digital Systems (RAU-6, 2023), at the Silesian University of Technology (SUT) in Gliwice, Poland, and at the Institute of Computer Science, the University of Silesia in Katowice in Sosnowiec, Poland.  
**Conflicts of Interest:** The authors declare no conflicts of interest.