A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Nadimi-Shahraki, Mohammad H.; Mohammadi, Saeed; Zamani, Hoda; Gandomi, Mostafa; Gandomi, Amir H.

doi:10.3390/electronics10243167

Cited by 13 publications

(7 citation statements)

References 103 publications

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“…However, MCAR is the most unrealistic assumption among the three mechanisms. To show realistic availability, in future, we will conduct missing value imputation experiments under MAR and MNAR situations, for example, using the datasets in the literature [ 21 ] or [ 23 ]. However, we need some important modifications in the proposed method to address MAR and MNAR situations.…”

Section: Resultsmentioning

confidence: 99%

Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Tada

Suzuki

Okada

2022

Entropy

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Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time.

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Section: Resultsmentioning

confidence: 99%

Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Tada

Suzuki

Okada

2022

Entropy

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“…According to the missing pattern, missing data are divided into three categories: missing completely at random (MCR), missing at random (MR), and missing not at random (NMR) [64,65]. In the MCR category, the missing data appear completely independent as isolated points and are randomly distributed.…”

Section: Preliminariesmentioning

confidence: 99%

“…In the MR category, the data are related to their neighboring points, so the missing data appear as a small set of consecutive points and at a particular time, which is a random distribution. In the NMR category, the missing data occur non-randomly throughout the dataset due to long-term errors in the detectors [65].…”

Section: Preliminariesmentioning

confidence: 99%

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

Gouran,

Nadimi-Shahraki,

Rahmani

et al. 2023

Remote Sensing

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In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.

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“…Another way to find missing academic data is to reach out to other researchers who may have access to such data. A lot of approaches have been applied for missing data imputation using a variety of algorithms and techniques that fit a value for the missing case(s) based on the overall behavior and pattern of the data population (Donders et al, 2006;Nadimi-Shahraki et al, 2021). Finding missing academic data can therefore require a combination of persistence, networking, and creative problem-solving using a variety of methods and resources.…”

Section: A Review Of Existing Missing Data Prediction Tools and Techn...mentioning

confidence: 99%

Ardp: Simplified Machine Learning Predictor for Missing Unidimensional Academic Results Dataset

Folorunso¹,

Akinyede

Agbele

2023

acs

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We present a machine learning predictor for academic results datasets (PARD), for missing academic results based on chi-squared expected calculation, positional clustering, progressive approximation of relative residuals, and positional averages of the data in a sampled population. Academic results datasets are data originating from academic institutions’ results repositories. It is a technique designed specifically for predicting missing academic results. Since the whole essence of data mining is to elicit useful information and gain knowledge-driven insights into datasets, PARD positions data explorer at this advantageous perspective. PARD promises to solve missing academic results dataset problems more quickly over and above what currently obtains in literatures. The predictor was implemented using Python, and the results obtained show that it is admissible in a minimum of up to 93.6 average percent accurate predictions of the sampled cases. The results demonstrate that PARD shows a tendency toward greater precision in providing the better solution to the problems of predictions of missing academic results datasets in universities.

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A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Cited by 13 publications

References 103 publications

Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

Ardp: Simplified Machine Learning Predictor for Missing Unidimensional Academic Results Dataset

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