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Found 10 papers, 4 papers with code
Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction
Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients.
PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions
Then, we apply it to study oscillation patterns in untimed genome-wide gene expression from 19 human brain regions of controls and AD patients.
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior.
On the algebraic structures of the space of interval-valued intuitionistic fuzzy numbers
In this study, we prove that the space of all IVIFNs with the relation in the method for comparing any two IVIFNs based on a score function and three types of entropy functions is a complete chain and obtain that this relation is an admissible order.
Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space
By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.
Top-$k$ Regularization for Supervised Feature Selection
Feature selection identifies subsets of informative features and reduces dimensions in the original feature space, helping provide insights into data generation or a variety of domain problems.
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction
Graph neural networks have been used for a variety of learning tasks, such as link prediction, node classification, and node clustering.
Fractal Autoencoders for Feature Selection
In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE).
Algorithmic Stability and Generalization of an Unsupervised Feature Selection Algorithm
Feature selection, as a vital dimension reduction technique, reduces data dimension by identifying an essential subset of input features, which can facilitate interpretable insights into learning and inference processes.
Distribution-dependent concentration inequalities for tighter generalization bounds
Concentration inequalities are indispensable tools for studying the generalization capacity of learning models.
