Modulo 9 model-based learning for missing data imputation

Ngueilbaye, Alladoumbaye; Wang, Hongzhi; Mahamat, Daouda Ahmat; Junaidu, Sahalu B.

doi:10.1016/j.asoc.2021.107167

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…Сучасний стан наукового напряму, орієнтованого на інтелектуальний аналіз даних та машинне навчання, визначає різке збільшення обсягу даних. Водночас виникає потреба щодо зменшення кількості атрибутів або/ та об'єктів за допомогою виокремлення найбільш інформативних параметрів для створення ефективних систем прогнозування, класифікації, кластеризації, прийняття рішень тощо [1][2][3][4]. Здебільшого експериментальні дані є «необробленими», неповними, мають різні діапазони змін значень атрибутів, і через це їх потрібно трансформувати або корегувати на етапі передобробки.…”

Section: модель оцінки якості нормалізації даних на основі застосування критеріїв якості класифікації об'єктівunclassified

Model of the data normalization quality evaluation on the basis of application of the objects classi-fication quality criteria

Yasinska-Damri¹,

Durnyak²

2021

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Model of the data normalization quality evaluation on the basis of application of the objects classi-fication quality criteria

Yasinska-Damri¹,

Durnyak²

2021

Print. Publish.

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“…On the other hand, missing value can be imputed by predictive models. Robust Machine Learning techniques such as Support Vector Machine(SVM), Linear Regression have been used for predicting missing data 10 . Multiple imputation(MI), based on predicted imputing, will create several different plausible imputed data sets and appropriately combining results obtained from each of them 11 .…”

Section: Introductionmentioning

confidence: 99%

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

Chang

Tong

et al. 2022

Preprint

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Handling missing data is indispensable in health-care real-world data processing. Imputing method may introduce error and multicollinearity. Therefore, we explored (Optimal Intact Subset Method, OIS.Method) to avoid the issues. By exploring an optimal deleting way of columns and rows with missing data, a subset retaining most information of original datasets was determined. Traditionally, we can traverse all deleting ways. But the computational cost is too high to use in large datasets. OIS.Method used an indicator to determine the optimal deleting order which can ascertain the optimal deleting way and simplify computing. In order to validate the effectiveness of OIS.Method, we compared OIS.Method with five other missing data handling methods in simulated real-world classification datasets. Additionally, we validated OIS.Method in two real-world classification tasks. In simulated datasets, the performance of OIS.Method was best(highest AUC was 1). In real-world datasets, OIS.Method could acquire better classification performance. Take AUC for an example: OIS.Method VS Simple Impute VS Random Forest VS Modified Random Forest, 0.8179±0.0005 VS 0.8116±0.0002 VS 0.8087±0.0009 VS 0.8093±0.0014 in task1, and 0.7028±0.0126 VS 0.6963±0.0231 VS 0.6957±0.0247 VS 0.6699±0.0249 in task2. The calculation of OIS.Method is smaller, and it is well-suited for large real-world datasets.

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“…In 1987, Rubin [5] proposed that missing data mechanisms fall into three categories: missing at random (MAR), missing completely at random (MCAR), and missing not at random (MNAR). Handling missing data appropriately is particularly important to ensure the accuracy of missing data imputation [6]. Scholars have conducted numerous studies, and the methods can be roughly divided into three categories: ignoring data or deletion, imputation, and statistical models.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method for the RUL of Equipment for Missing Data

et al. 2021

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We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short-term memory (MSDCNN-LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN-LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.

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Modulo 9 model-based learning for missing data imputation

Cited by 27 publications

References 44 publications

Model of the data normalization quality evaluation on the basis of application of the objects classi-fication quality criteria

Model of the data normalization quality evaluation on the basis of application of the objects classi-fication quality criteria

A novel method for handling missing data in health care real-world study: Optimal Intact Subset Method

A Prediction Method for the RUL of Equipment for Missing Data

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