Search: imputation - ai.jp.net

Research #Machine Learning 📝 BlogAnalyzed: Jan 3, 2026 06:58

Is 399 rows × 24 features too small for a medical classification model?

Published:Jan 3, 2026 05:13

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1 min read

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r/learnmachinelearning

Analysis

The article discusses the suitability of a small tabular dataset (399 samples, 24 features) for a binary classification task in a medical context. The author is seeking advice on whether this dataset size is reasonable for classical machine learning and if data augmentation is beneficial in such scenarios. The author's approach of using median imputation, missingness indicators, and focusing on validation and leakage prevention is sound given the dataset's limitations. The core question revolves around the feasibility of achieving good performance with such a small dataset and the potential benefits of data augmentation for tabular data.

Key Takeaways

•The dataset size (399 samples, 24 features) is small, potentially limiting model performance.
•Classical ML techniques are likely the most appropriate approach, given the dataset size.
•Data augmentation for tabular data at this scale is questionable and may not yield significant improvements.
•Focusing on robust validation and leakage prevention is crucial due to the risk of overfitting.

Reference

“The author is working on a disease prediction model with a small tabular dataset and is questioning the feasibility of using classical ML techniques.”

Permalink r/learnmachinelearning

Paper #Machine Learning, Statistics 🔬 ResearchAnalyzed: Jan 3, 2026 09:27

Robust Reduced Rank Regression for Heavy-Tailed Noise and Missing Data

Published:Dec 30, 2025 20:09

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of classical Reduced Rank Regression (RRR) methods, which are sensitive to heavy-tailed errors, outliers, and missing data. It proposes a robust RRR framework using Huber loss and non-convex spectral regularization (MCP and SCAD) to improve accuracy in challenging data scenarios. The method's ability to handle missing data without imputation and its superior performance compared to existing methods make it a valuable contribution.

Key Takeaways

•Proposes a robust RRR framework to handle heavy-tailed noise, outliers, and missing data.
•Combines Huber loss with non-convex spectral regularization (MCP and SCAD).
•Handles missing data without imputation.
•Outperforms existing methods in simulations and real-world data.
•Provides an R package (rrpackrobust) for implementation.

Reference

“The proposed methods substantially outperform nuclear-norm-based and non-robust alternatives under heavy-tailed noise and contamination.”

Permalink ArXiv

Research Paper #Time Series Analysis, Generative Models, Imputation 🔬 ResearchAnalyzed: Jan 3, 2026 16:00

Bridge-TS: Improving Time Series Imputation with Enhanced Priors

Published:Dec 29, 2025 19:52

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of time series imputation, a crucial task in various domains. It innovates by focusing on the prior knowledge used in generative models. The core contribution lies in the design of 'expert prior' and 'compositional priors' to guide the generation process, leading to improved imputation accuracy. The use of pre-trained transformer models and the data-to-data generation approach are key strengths.

Key Takeaways

Reference

“Bridge-TS reaches a new record of imputation accuracy in terms of mean square error and mean absolute error, demonstrating the superiority of improving prior for generative time series imputation.”

Permalink ArXiv

Data Science #Data Preprocessing 📝 BlogAnalyzed: Dec 28, 2025 13:00

AI-Driven Data Analysis - Data Preprocessing (22) ② - Missing Value Handling: Missing Value Imputation with Classification Models

Published:Dec 28, 2025 12:44

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1 min read

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Qiita AI

Analysis

This article discusses using AI, specifically classification models, to handle missing data during the data preprocessing stage of AI-driven data analysis. It's the second part of a series focusing on data preprocessing. The article likely covers the methodology of using classification models to predict and impute missing values, potentially comparing it to other imputation techniques. The mention of Gemini suggests the use of Google's AI model for some aspect of the process, possibly for generating code or assisting in the analysis. The inclusion of Python implementation indicates a practical, hands-on approach to the topic. The article's structure includes an introduction to the data used, the Python implementation, the use of Gemini, and a summary.

Key Takeaways

•Using classification models for missing value imputation.
•Python implementation for practical application.
•Leveraging Gemini AI for data analysis tasks.

Reference

“AIでデータ分析-データ前処理(22)②-欠損処理：分類モデルによる欠損補完”

Permalink Qiita AI

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 12:31

AI Data Analysis - Data Preprocessing (22) - Missing Value Handling: Missing Value Completion by Regression Model

Published:Dec 27, 2025 12:11

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1 min read

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Qiita AI

Analysis

This article discusses using AI, specifically regression models, to handle missing values in data preprocessing for AI data analysis. It mentions using Python for implementation and Gemini for AI utilization. The article likely provides a practical guide on how to implement this technique, potentially including code snippets and explanations of the underlying concepts. The focus is on a specific method (regression models) for addressing a common data issue (missing values), suggesting a hands-on approach. The mention of Gemini implies the integration of a specific AI tool to enhance the process. Further details would be needed to assess the depth and novelty of the approach.

Key Takeaways

•Using regression models for missing value imputation.
•Implementation in Python.
•AI utilization with Gemini.
•Focus on data preprocessing techniques.

Reference

“AIでデータ分析-データ前処理(22)-欠損処理：回帰モデルによる欠損補完”

Permalink Qiita AI

Research Paper #Time-Series Forecasting 🔬 ResearchAnalyzed: Jan 3, 2026 16:25

TimePerceiver: A Unified Framework for Time-Series Forecasting

Published:Dec 27, 2025 10:34

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1 min read

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ArXiv

Analysis

This paper introduces TimePerceiver, a novel encoder-decoder framework for time-series forecasting. It addresses the limitations of prior work by focusing on a unified approach that considers encoding, decoding, and training holistically. The generalization to diverse temporal prediction objectives (extrapolation, interpolation, imputation) and the flexible architecture designed to handle arbitrary input and target segments are key contributions. The use of latent bottleneck representations and learnable queries for decoding are innovative architectural choices. The paper's significance lies in its potential to improve forecasting accuracy across various time-series datasets and its alignment with effective training strategies.

Key Takeaways

Reference

“TimePerceiver is a unified encoder-decoder forecasting framework that is tightly aligned with an effective training strategy.”

Permalink ArXiv

Research #Causal Inference 🔬 ResearchAnalyzed: Jan 10, 2026 08:58

PIPCFR: Estimating Treatment Effects with Post-Treatment Variables

Published:Dec 21, 2025 13:57

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1 min read

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ArXiv

Analysis

This ArXiv paper introduces a novel method (PIPCFR) for estimating individual treatment effects. The focus on handling post-treatment variables is particularly relevant in causal inference, where traditional methods can be biased.

Key Takeaways

•PIPCFR is a new method for estimating individual treatment effects.
•The method addresses the challenge of post-treatment variables.
•The research is published on ArXiv, suggesting early-stage development.

Reference

“PIPCFR: Pseudo-outcome Imputation with Post-treatment Variables for Individual Treatment Effect Estimation”

Permalink ArXiv

Research #Interpretable ML 🔬 ResearchAnalyzed: Jan 10, 2026 09:30

Analyzing Uncertainty in Interpretable Machine Learning

Published:Dec 19, 2025 15:24

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1 min read

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ArXiv

Analysis

The ArXiv article likely explores the complexities of handling uncertainty within interpretable machine learning models, which is crucial for building trustworthy AI. Understanding imputation uncertainty is vital for researchers and practitioners aiming to build robust and reliable AI systems.

Key Takeaways

•Focuses on uncertainty quantification within interpretable ML methods.
•Addresses the challenges of dealing with missing data or incomplete information.
•Contributes to building more trustworthy and reliable AI systems.

Reference

“The article is sourced from ArXiv, indicating a pre-print or research paper.”

Permalink ArXiv

Research #traffic management 🔬 ResearchAnalyzed: Jan 4, 2026 09:23

A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locations

Published:Dec 19, 2025 14:14

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1 min read

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ArXiv

Analysis

This article presents a research paper on a specific application of AI in traffic management. The focus is on using a hybrid network to predict traffic flow in areas where data is not directly collected. The approach combines inductive and transductive learning methods, which is a common strategy in machine learning to leverage both general patterns and specific instance information. The title clearly states the problem and the proposed solution.

Key Takeaways

•The research focuses on traffic flow prediction.
•It utilizes a hybrid inductive-transductive network.
•The goal is to impute traffic flow at unsampled locations.

Reference

“”

Permalink ArXiv

Research #Multi-view 🔬 ResearchAnalyzed: Jan 10, 2026 10:21

Unsupervised Multi-view Learning: A Deep Dive into Feature and Instance Selection

Published:Dec 17, 2025 16:29

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1 min read

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ArXiv

Analysis

The research focuses on unsupervised learning techniques for multi-view data, addressing the challenge of feature and instance selection. The cross-view imputation method presents a potentially novel approach to handle missing data and improve model performance within this framework.

Key Takeaways

•Addresses unsupervised learning problems in multi-view data.
•Proposes a novel method involving cross-view imputation.
•Focuses on feature and instance co-selection.

Reference

“The article is sourced from ArXiv, indicating it's likely a research paper.”

Permalink ArXiv

Research #TimeSeries 🔬 ResearchAnalyzed: Jan 10, 2026 10:32

FADTI: Advanced Time Series Imputation with Fourier and Attention

Published:Dec 17, 2025 06:16

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1 min read

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ArXiv

Analysis

This research introduces a novel approach to multivariate time series imputation using Fourier transforms and attention mechanisms. The focus on diffusion models suggests a potential improvement over existing imputation techniques by leveraging the strengths of these advanced techniques.

Key Takeaways

•Proposes a new model called FADTI for time series imputation.
•Utilizes Fourier transforms and attention mechanisms.
•The research likely targets improved accuracy and performance in time series data handling.

Reference

“The article's source is ArXiv, indicating a research paper.”

Permalink ArXiv

Research #Clustering 🔬 ResearchAnalyzed: Jan 10, 2026 12:06

Selective Imputation for Multi-view Clustering: A Promising Approach

Published:Dec 11, 2025 06:22

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1 min read

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ArXiv

Analysis

The ArXiv article discusses a method for handling incomplete data in multi-view clustering. The focus on selective imputation suggests a potentially efficient approach compared to more comprehensive methods.

Key Takeaways

•Addresses the challenge of incomplete data in multi-view clustering.
•Proposes a 'selective imputation' approach, suggesting efficiency.
•Published on ArXiv, indicating early-stage research.

Reference

“The article's context revolves around selective imputation for incomplete multi-view clustering.”

Permalink ArXiv

Is 399 rows × 24 features too small for a medical classification model?

Analysis

Key Takeaways

Robust Reduced Rank Regression for Heavy-Tailed Noise and Missing Data

Analysis

Key Takeaways

Bridge-TS: Improving Time Series Imputation with Enhanced Priors

Analysis

Key Takeaways

AI-Driven Data Analysis - Data Preprocessing (22) ② - Missing Value Handling: Missing Value Imputation with Classification Models

Analysis

Key Takeaways

AI Data Analysis - Data Preprocessing (22) - Missing Value Handling: Missing Value Completion by Regression Model

Analysis

Key Takeaways

TimePerceiver: A Unified Framework for Time-Series Forecasting

Analysis

Key Takeaways

PIPCFR: Estimating Treatment Effects with Post-Treatment Variables

Analysis

Key Takeaways

Analyzing Uncertainty in Interpretable Machine Learning

Analysis

Key Takeaways

A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locations

Analysis

Key Takeaways

Unsupervised Multi-view Learning: A Deep Dive into Feature and Instance Selection

Analysis

Key Takeaways

FADTI: Advanced Time Series Imputation with Fourier and Attention

Analysis

Key Takeaways

Selective Imputation for Multi-view Clustering: A Promising Approach

Analysis

Key Takeaways

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