Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Karim, Faten Khalid; Elmannai, Hela; Seleem, Abdelrahman; Hamad, Safwat; Mostafa, Samih M.

doi:10.3390/electronics11233929

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Also, used the FS to find the cluster of comparable data and valuation them. [22] proposes an imputation method based on Fuzzy system and entropy measurement, to select the elect features (or patterns) loading missing data assistance the forecast missing data within the election feature utilizing the "Bayesian Ridge Regression" BRR. In their technique the missing data values are manipulated within other patterns in accumulative order, the loaded patterns are combined within BRR equation to predict the missing data for the next elected missing pattern.…”

Section: Related Workmentioning

confidence: 99%

“…-The Fuzzy System is used to find the cluster of comparable data and valuating them. [22] Fuzzy System, entropy measurements -Selects the elect features (or patterns) loading missing data assistance the forecast missing data within the election feature by utilizing the BRR [23] Multilayer perception and deep belief network -Predicts incomplete values in continuous data and compare their performance to the discretization data [24] Auto encoder -Predicts missing data and peripheralization over missing values in a shared model of common variables and outcomes [25] Adaptive multiple imputation of missing value (Clustering)…”

Section: Table 1 a Summarary Of Existing Imputation Based Machine Lea...mentioning

confidence: 99%

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A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale several applications to collect vast amount of data from different locations in a time-series manner. However, collected data may be missing or damaged due to several issues such as unreliable communications, faulty sensors and synchronization problem that decrees application accuracy. Therefore, a several imputation-based machine learning approaches have been suggested to handle this problem in IoT application. In this study, a new approach is proposed called impute missing data (IMD) based on multi-Spike Neural Network learning method called IMD-SNN, to increase the reliability of missing value imputation in IoT. The method consists of three phases: Inserting missing data, to evaluate the missing values based on the cumulative distribution function (CDF), the multi SNN phase to estimate missing data according to the timestamp and a performance evaluation phase to evaluate an imputation accuracy via made a comparison with two models: imputation based KNN (I-KNN) and Imputation based (I-MLP) model based on resource usage and imputation accuracy assessment metrics. The implementation results have been shown that IMD-SNN utilizes less energy usage in comparison with (I-MLP) model and I-KNN model and gives highest imputation accuracy in contrast with (I-MLP) model and I-KNN model. Also, the IMD-SNN model utilizes less memory usage and needs execution time less than I-MLP model. INDEX TERMS Internet of Thing (IoT), Spike Neural Network (SNN), Multilayer Perceptron (MLP), Root Mean Square Error (RMSE)

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Section: Related Workmentioning

confidence: 99%

Section: Table 1 a Summarary Of Existing Imputation Based Machine Lea...mentioning

confidence: 99%

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods

Palanivinayagam

Damaševičius

2023

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The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.

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Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Cited by 2 publications

References 45 publications

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods

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