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Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Updated: Nov 15, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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DLIN: Deep Ladder Imputation Network.

Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif

IEEE Transactions on Cybernetics

|March 4, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep learning algorithm for missing data imputation, offering superior generalization across diverse applications and high missing data ratios. The method effectively handles various missing data patterns, outperforming existing techniques.

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Published on: August 16, 2020

Area of Science:

Data Science
Machine Learning
Artificial Intelligence

Background:

Addressing data loss is crucial across many domains.
Existing task-specific solutions lack generalizability for missing data estimation.
High missing ratios and incomplete training sets pose significant challenges.

Purpose of the Study:

To propose a novel, generalized missing data imputation algorithm.
To develop a method effective for very high missing ratios and incomplete training data.
To enhance flexibility against various missing data patterns and distributions.

Main Methods:

A novel deep neural network formulation integrating denoising autoencoders and ladder architecture.
Utilizing both complete and incomplete data parts for imputation.
Nonparametric approach for flexibility with diverse data distributions and missingness.

Main Results:

The proposed algorithm demonstrates superior generalization ability on real-world benchmark datasets.
Effectiveness shown across three missing data mechanisms: MCAR, MAR, and MNAR.
Outperforms reputable imputation techniques in comparative studies, including on a cyber-physical system.

Conclusions:

The novel imputation algorithm offers a robust and flexible solution for missing data problems.
Its generalization capability makes it suitable for a wide range of applications, even with high missing data.
The method advances the field of missing data imputation, particularly for challenging scenarios.