Abstract
In popular approaches to classification by aggregating decisions, there are two main trends. One path leads to the construction of a classifier ensemble, where a group of diversified inducers vote on a label to be assigned a sample. The second direction is to obtain a decision based on dispersed data, through some form of information fusion. The paper proposes a new mode of operation for a voting classifier, where one and the same inducer can reach a final decision relaying on labels assigned through partially dispersed data, but also different forms of the same data, resulting from discretisation. The experiments were carried out on several datasets, classifiers, and algorithms for aggregating decisions. They resulted in observation of cases and scenarios for improved predictions, showing the merits of the presented research methodology.
Paper Type
Short Paper
DOI
10.62036/ISD.2024.122
Voting Classifier using Discretisation in Aggregating Decisions
In popular approaches to classification by aggregating decisions, there are two main trends. One path leads to the construction of a classifier ensemble, where a group of diversified inducers vote on a label to be assigned a sample. The second direction is to obtain a decision based on dispersed data, through some form of information fusion. The paper proposes a new mode of operation for a voting classifier, where one and the same inducer can reach a final decision relaying on labels assigned through partially dispersed data, but also different forms of the same data, resulting from discretisation. The experiments were carried out on several datasets, classifiers, and algorithms for aggregating decisions. They resulted in observation of cases and scenarios for improved predictions, showing the merits of the presented research methodology.
Recommended Citation
Stanczyk, U., Zielosko, B. & Baron, G. (2024). Voting Classifier using Discretisation in Aggregating Decisions. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.122