DL-GSA: A Deep Learning Metaheuristic Approach to Missing Data Imputation

Garg, Ayush; Naryani, Deepika; Aggarwal, Garvit; Aggarwal, Swati

doi:10.1007/978-3-319-93818-9_49

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…In addition, some recently developed methods that can handle mixed missing data are SICE [24], FEMI [25], and HCMM-LD [26]. When the sample sizes are large enough compared to the number of features, deep learning techniques such as Multiple Imputation Using Deep Denoising Autoencoders [15], DL-GSA [7], and Swarm Intelligence-Deep Neural Network [27] can be powerful imputers. However, it is worth noting that deep learning methods usually require more data than statistical imputation approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Despite recent efforts in directly handling missing data [1][2][3][4], missing data imputation approaches [5][6][7] remain commonly used. This is because directly handling missing data can be complicated and usually are developed for specific target problems or models, while imputation can be more versatile.…”

Section: Introductionmentioning

confidence: 99%

“…Many techniques have been developed for missing data imputation, ranging from traditional techniques such as MICE [5], K-Nearest Neighbors to recent machine learning/deep learning techniques such as GAIN [6], DL-GSA [7]. However, many of them can be computationally expensive for big datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Principal Component Analysis based frameworks for efficient missing data imputation algorithms

Nguyen¹,

Ly²,

Riegler³

et al. 2022

Preprint

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Missing data is a commonly occurring problem in practice, and imputation, i.e., filling the missing entries of the data, is a popular way to deal with this problem. This motivates multiple works on imputation to deal with missing data of various types and dimensions. However, for high-dimensional datasets, these imputation methods can be computationally expensive. Therefore, in this work, we propose Principle Component Analysis Imputation (PCAI), a simple framework based on Principle Component Analysis (PCA) to speed up the imputation process of many available imputation techniques. Next, based on PCAI, we propose PCA Imputation -Classification (PIC), an imputation-dimension reductionclassification framework to deal with missing data classification problems where it is desirable to reduce the dimensions before training a classification model. Our experiments show that the proposed frameworks can be utilized with various imputation algorithms and improve the imputation speed significantly. Interestingly, the frameworks aid imputation methods that rely on many parameters by reducing the dimension of the data and hence, reducing the number of parameters needed to be estimated. Moreover, they not only can achieve compatible mean square error/higher classification accuracy compared to the traditional imputation style on the original missing dataset but many times deliver even better results. In addition, the frameworks also help to tackle the memory issue that many imputation approaches have by reducing the number of features.Preprint. Under review.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Principal Component Analysis based frameworks for efficient missing data imputation algorithms

Nguyen¹,

Ly²,

Riegler³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, deep learning techniques also form another dominant class of imputation methods. Those techniques include multiple imputations using Deep Denoising Autoencoders [26], imputation via Stacked Denoising Autoencoders [8], imputation via Adversarially-trained Graph Convolutional Networks [27], a Swarm Intelligence-deep neural network [28], combining Gravitational search algorithm with a deep-autoencoder [29], Generative Adversarial Multiple Imputation Network [30], etc. Hence, they are not applicable to small data sets.…”

Section: Related Workmentioning

confidence: 99%

EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data

Nguyen

et al. 2021

Information Sciences

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The missing data problem has been broadly studied in the last few decades and has various applications in different areas such as statistics or bioinformatics.Even though many methods have been developed to tackle this challenge, most of those are imputation techniques that require multiple iterations through the data before yielding convergence. In addition, such approaches may introduce extra biases and noises to the estimated parameters. In this work, we propose novel algorithms to find the maximum likelihood estimates (MLEs) for a oneclass/multiple-class randomly missing data set under some mild assumptions.As the computation is direct without any imputation, our algorithms do not require multiple iterations through the data, thus promising to be less timeconsuming than other methods while maintaining superior estimation performance. We validate these claims by empirical results on various data sets of different sizes and release all codes in a GitHub repository to contribute to the research community related to this problem.

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“…Deep learning techniques also form another dominant class of imputation methods. Those techniques include multiple imputations using Deep Denoising Autoencoders [32], imputation via Stacked Denoising Autoencoders [12], imputation via Adversarially-trained Graph Convolutional Networks [33], a Swarm Intelligence-deep neural network [34], combining Gravitational search algorithm with a deep-autoencoder [35]. However, a significant drawback of deep learning methods is the need for a lot of data.…”

Section: Related Workmentioning

confidence: 99%

EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data

Nguyen¹,

Nguyen²,

Nguyen³

et al. 2020

Preprint

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The problem of monotone missing data has been broadly studied during the last two decades and has many applications in different fields such as bioinformatics or statistics. Commonly used imputation techniques require multiple iterations through the data before yielding convergence. Moreover, those approaches may introduce extra noises and biases to the subsequent modeling. In this work, we derive exact formulas and propose a novel algorithm to compute the maximum likelihood estimators (MLEs) of a multiple class, monotone missing dataset when all the covariance matrices of all categories are assumed to be equal, namely EPEM. We then illustrate an application of our proposed methods in Linear Discriminant Analysis (LDA). As the computation is exact, our EPEM algorithm does not require multiple iterations through the data as other imputation approaches, thus promising to handle much less time-consuming than other methods. This effectiveness was validated by empirical results when EPEM reduced the error rates significantly and required a short computation time compared to several imputation-based approaches. We also release all codes and data of our experiments in one GitHub repository to contribute to the research community related to this problem.

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DL-GSA: A Deep Learning Metaheuristic Approach to Missing Data Imputation

Cited by 7 publications

References 10 publications

Principal Component Analysis based frameworks for efficient missing data imputation algorithms

Principal Component Analysis based frameworks for efficient missing data imputation algorithms

EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data

EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data

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