scholarly journals A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

2021 ◽  
Vol 11 (8) ◽  
pp. 3719
Author(s):  
Sun-Young Ihm ◽  
Shin-Eun Lee ◽  
Young-Ho Park ◽  
Aziz Nasridinov ◽  
Miyeon Kim ◽  
...  
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
High Reliability ◽  
User Preferences ◽  
User Preference ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Common Interests ◽  
Recommendation Accuracy

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

scholarly journals Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

2020 ◽  
Vol 69 ◽  
pp. 1255-1285
Author(s):  
Ricardo Cardoso Pereira ◽  
Miriam Seoane Santos ◽  
Pedro Pereira Rodrigues ◽  
Pedro Henriques Abreu
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
State Of The Art ◽  
Data Imputation ◽  
Tabular Data ◽  
Missing Data Imputation ◽  
Learning Techniques ◽  
Real World Datasets ◽  
And Training ◽  
Machine Learning Models

Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.

scholarly journals Complex Data Imputation by Auto-Encoders and Convolutional Neural Networks—A Case Study on Genome Gap-Filling

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 37 ◽  
Author(s):  
Luca Cappelletti ◽  
Tommaso Fontana ◽  
Guido Walter Di Donato ◽  
Lorenzo Di Tucci ◽  
Elena Casiraghi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
State Of The Art ◽  
The State ◽  
Complex Data ◽  
Data Imputation ◽  
Genome Sequences ◽  
Missing Data Imputation ◽  
The Past ◽  
Learning Techniques

Missing data imputation has been a hot topic in the past decade, and many state-of-the-art works have been presented to propose novel, interesting solutions that have been applied in a variety of fields. In the past decade, the successful results achieved by deep learning techniques have opened the way to their application for solving difficult problems where human skill is not able to provide a reliable solution. Not surprisingly, some deep learners, mainly exploiting encoder-decoder architectures, have also been designed and applied to the task of missing data imputation. However, most of the proposed imputation techniques have not been designed to tackle “complex data”, that is high dimensional data belonging to datasets with huge cardinality and describing complex problems. Precisely, they often need critical parameters to be manually set or exploit complex architecture and/or training phases that make their computational load impracticable. In this paper, after clustering the state-of-the-art imputation techniques into three broad categories, we briefly review the most representative methods and then describe our data imputation proposals, which exploit deep learning techniques specifically designed to handle complex data. Comparative tests on genome sequences show that our deep learning imputers outperform the state-of-the-art KNN-imputation method when filling gaps in human genome sequences.

Evaluating the state-of-the-art in missing data imputation for clinical data (Preprint)

2021 ◽  
Author(s):  
Yuan Luo
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Clinical Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Ground Truth ◽  
The State ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Clinical Dataset

UNSTRUCTURED The Data Analytics Challenge on Missing data Imputation (DACMI) presented a shared clinical dataset with ground truth for evaluating and advancing the state-of-the-art in imputing missing data for clinical time series. The challenge attracted 12 international teams spanning three continents across multiple industries and academia. The challenge participating systems practically advanced the state-of-the-art with considerable margins, and their designing principles will inform future efforts to better model clinical missing data.

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