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Interval Dominance-Based Feature Selection for Interval-Valued Ordered Data
Summary
This study introduces new methods for feature selection in interval-valued ordered decision systems (IV-ODS). It develops novel thresholds for interval dominance and overlap degrees to extend dominance principles for multivalued data, enhancing rough set approaches.
Area of Science:
- Data Mining
- Rough Set Theory
- Machine Learning
Background:
- Dominance-based rough approximation effectively handles ordered criteria with single-valued attributes.
- Extending dominance principles to multivalued data in ordered decision systems (ODS) presents a significant challenge for feature selection.
Purpose of the Study:
- To adapt the dominance principle for interval-valued ordered decision systems (IV-ODS).
- To develop novel thresholds for interval dominance and overlap degrees.
- To establish an interval-valued dominance-based rough set approach (IV-DRSA) and feature selection methods for IV-ODS.
Main Methods:
- Introduction of Interval Dominance Degree (IDD) and Interval Overlap Degree (IOD) thresholds.
- Construction of an interval-valued dominance relation using IDD and IOD.
- Development of interval-valued dominance-based feature selection rules and algorithms.
Main Results:
- The proposed IDD and IOD thresholds enable the application of dominance principles to interval-valued data.
- The interval-valued dominance relation and IV-DRSA were successfully investigated.
- Feature selection rules and algorithms tailored for IV-ODS were established.
Conclusions:
- The developed methods provide a robust framework for feature selection in IV-ODS.
- Experimental validation on UCI datasets demonstrates the effectiveness of the proposed feature selection rules.
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