Tltd: Transfer Learning for Tabular Data

Bragilovski, Maxim; Kapri, Zahi; Rokach, Lior; Levy-Tzedek, Shelly

doi:10.2139/ssrn.4333423

Cited by 1 publication

(2 citation statements)

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“…In summary, these examples illustrate the broad applicability of DeepInsight beyond simple classification tasks. It has been effectively utilized for feature selection 27,28 , integrated with methods like XGBoost and Random Forest 29,30 , and ViTs 26 , employed in identifying gene subsets for pathway analysis 8 , and applied for multi-omics data analysis 7 . Additionally, DeepInsight has been used in generating data through GANs 30,31 , and processing multi-omics data across multiple layers 7 .…”

Section: Discussionmentioning

confidence: 99%

“…The applications of DeepInsight, however, extend beyond just classification tasks. It has been effectively used for feature selection 27,28 , integrated with the methods like XGBoost and random forest 29,30 , identifying gene subsets for pathway analysis 8 , generating data through GANs 30,31 , and processing multi-omics data via multiple layers 7 . This wide range of applications highlights the potential of MRep-DeepInsight as a generalized tool for diverse data analysis and interpretation.…”

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confidence: 99%

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Enhanced analysis of tabular data through Multi-representation DeepInsight

Sharma,

López,

Jia

et al. 2024

Sci Rep

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Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields.

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