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1366. Auto-Pruned SOCP Clustering Problem
Invited abstract in session MD-4: Hybrid Appraches in Deep Learning and Machine Learning, stream Recent Advancements in AI .
Monday, 14:30-16:00Room: 1001 (building: 202)
Authors (first author is the speaker)
| 1. | Duygu Üçüncü
|
| Bahcesehir University | |
| 2. | Sureyya Ozogur-Akyuz
|
| Department of Mathematics Engineering, Bahcesehir University |
Abstract
The selection of the best models in the ensemble has a pivotal role in the overall performance of ensemble learning algorithms due to the fact that redundant solutions in the ensemble library will decrease the overall prediction accuracy. Therefore, on account of eliminating such candidates, accuracy and diversity should be taken into consideration.
This study proposes a novel ensemble clustering method using a second-order conic optimization model that continuously and convexly solves integer programming for decision-making in various disciplines. The proposed model optimizes the balance between accuracy and diversity, resulting in the selection of the best candidates for prediction. The remarkable contribution of the study is the automatic sub-ensemble selection while optimizing for accuracy and diversity simultaneously. The model has been tested with real-world data and has achieved competitive prediction performance.
Keywords
- Machine Learning
- Optimization Modeling
- Decision Support Systems
Status: accepted
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