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Binary Feature Mask Optimization for Feature Selection

Autor: Lorasdagi, Mehmet E., Turali, Mehmet Y., Koc, Ali T., Kozat, Suleyman S.

We investigate feature selection problem for generic machine learning (ML) models. We introduce a novel framework that selects features considering the predictions of the model. Our framework innovates by using a novel feature masking approach to eli

Externí odkaz: http://arxiv.org/abs/2401.12644

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AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking

Autor: Turali, Mehmet Y., Lorasdagi, Mehmet E., Koc, Ali T., Kozat, Suleyman S.

We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature selection methods, however, these methods face challenges such as scalabil

Externí odkaz: http://arxiv.org/abs/2401.11250

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Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting

Autor: Tumay, Aysin, Aydin, Mustafa E., Koc, Ali T., Kozat, Suleyman S.

We introduce a novel ensemble approach for feature selection based on hierarchical stacking for non-stationarity and/or a limited number of samples with a large number of features. Our approach exploits the co-dependency between features using a hier

Externí odkaz: http://arxiv.org/abs/2310.17544

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An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories

Autor: Harsha, Pavithra, Subramanian, Shivaram, Koc, Ali, Ramakrishna, Mahesh, Quanz, Brian, Shah, Dhruv, Narayanaswami, Chandra

We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robu

Externí odkaz: http://arxiv.org/abs/2310.12183

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