Correction to: Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data

Silva-Ramírez, Esther-Lydia; Cabrera-Sánchez, Juan-Francisco

doi:10.1007/s00521-021-06623-1

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Silva-Ramirez et al (7) initiated the CANFIS-ART technique to automate data imputation, which is using the Co-active Neuro-Fuzzy Inference System. The Fuzzy-ART technique is used to create this model, consisting of Neural Network adaptive abilities and a fuzzy logic qualitative method.…”

Section: Related Workmentioning

confidence: 99%

“…The ratio of True Positives to all Positives is known as precision. Precision measures exactness or quality and a positive predictive value, as illustrated in Equation (7).…”

Section: Precisionmentioning

confidence: 99%

“…T P (T P + FP) (7) Table 2 demonstrates the findings attained before and after missing data was imputed based on Precision value using the TT-MDI algorithm. Table 2 compares precision before and after employing the TT-MDI algorithm to fill in missing data.…”

Section: Precision =mentioning

confidence: 99%

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A Novel Two Tier Missing at Random Type Missing Data Imputation using Enhanced Linear Interpolation Technique on Internet of Medical Things

Punitha¹,

Sathiaseelan²

2023

IJST

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Objectives: Data collection and distribution are essential components required for the victory of Internet of Medical Things (IoMT) system. Generally, missing data is the most recurrent problem that impacts an overall system performance. Methods: Missing data in IoMT systems can be caused by various factors, including faulty connections, external attacks, or sensing errors. Although missing data is ubiquitous in IoT, missing data imputation is hardly ever observed in an IoMT setting. As a result, doing analytics on IoMT data with missing values causes a deterioration in the accuracy and dependability of the data analysis outputs. To achieve excellent performance, missing data must be imputed once it occurs in such systems. Therefore, this paper proposes a novel Two Tier Missing Data Imputation (TT-MDI) technique for missing at random (MAR) type missing data in IoMT using an enhanced linear interpolation technique. Findings: The proposed TT-MDI algorithm has two tiers for imputing MAR missing data and it was tested using the Kaggle Machine Learning Repository's cStick IoMT dataset. Utilizing the distances between the class centroids with their related data instances, the first tier aims to identify the imputation threshold. The identified threshold is then used by the second tier to impute missing data. According to the experimental findings, the proposed work offers higher accuracy, precision, recall, and F-measure for imputed dataset using the TT-MDI technique than missing data included dataset when compared to the original dataset. Novelty: The TT-MDI technique consists of two tiers. The first tier uses Manhattan distances between class centroids and related data instances to discover the imputation threshold. Next, the second tier uses the discovered threshold to impute missing data using the Enhanced Linear Interpolation technique.

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Section: Related Workmentioning

confidence: 99%

“…The ratio of True Positives to all Positives is known as precision. Precision measures exactness or quality and a positive predictive value, as illustrated in Equation (7).…”

Section: Precisionmentioning

confidence: 99%