A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a new feature selection method integrating feature relationships.
The use of variable selection methods is particularly appealing in statistical problems with functional data. The obvious general criterion for variable selection is to choose the `most representative' or `most relevant' variables. However, it is also clear that a purely relevance-oriented criterion could lead to selec…
Efficiently predict LLM benchmarks using feature selection and regression.
DC-SIS selects features faster than mRMR for Parkinson's vocal diagnosis.
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant fe…
Improved fault diagnosis for bearings using mRMR and transfer learning.
Proposes a copula-based filter for diabetes risk prediction.
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study,…
Paper predicts Indian stocks using news psycholinguistic features.
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individu…
Proposes a new framework for predicting stock market movements using sparse neural architectures.
This manuscript presents the following: (1) an improved version of the Binary Simultaneous Perturbation Stochastic Approximation (SPSA) Method for feature selection in machine learning (Aksakalli and Malekipirbazari, Pattern Recognition Letters, Vol. 75, 2016) based on non-monotone iteration gains computed via the Barz…
AutoFS combines trainers to improve feature selection efficiency and effectiveness.
Study uses machine learning to identify IBD biomarkers from gut microbiota.