Search Results - (( java implementation phase algorithm ) OR ( missing data learning algorithm ))
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Systematic review on missing data imputation techniques with machine learning algorithms for healthcare
Published 2022“…Many machine learning algorithms have been applied to impute missing data with plausible values. …”
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…So, the application of the theory as part of the learning models was proposed in this thesis. Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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ExtraImpute: a novel machine learning method for missing data imputation
Published 2022“…Its existence usually leads to undesirable results while conducting data analysis using machine learning methods. Recently, researchers have proposed several imputation approaches to deal with missing values in real-world datasets. …”
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Imputation Analysis of Time-Series Data Using a Random Forest Algorithm
Published 2024“…Missing data poses a significant challenge in extensive datasets, particularly those containing time-series information, leading to potential inaccuracies in data analysis and machine learning model development. …”
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A novel approach for handling missing data to enhance network intrusion detection system
Published 2025“…To address this issue, we introduce DeepLearning_Based_MissingData_Imputation (DMDI), a novel method designed to enhance the quality of input data by efficiently handling missing values. …”
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Tangible interaction learning model to enhance learning activity processes among children with dyslexia
Published 2024“…Missing data is a widespread data quality issue across various domains. …”
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Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset
Published 2020“…This can significantly affect the prediction results and making it less accurate due to missing value and become a challenging issue. Missing values in datasets is a synonymous problem in data mining which could lead to an incomplete dataset, making inaccurate predictions results in machine learning prediction processes. …”
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Intelligent imputation method for mix data-type missing values to improve data quality
Published 2024“…Missing data is a widespread data quality issue across various domains. …”
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Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…All the algorithm for the engine has been developed by using Java script language. …”
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Machine learning model for performance prediction in mobile network management / Muhammad Hazim Wahid
Published 2022“…The methodology includes drive test measurement for data collection, exploratory data analysis, data preparation, and applying machine learning algorithms to predict mobile network performance. …”
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Extreme learning machine classification of file clusters for evaluating content-based feature vectors
Published 2018“…In the digital forensic investigation and missing data files retrieval in general, there is a challenge of recovering files that have missing system information. …”
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An Apriori-based Data Analysis on Suspicious Network Event Recognition
Published 2019“…Then, each missing value in the test data set is decided by using the obtained rules. …”
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Classification of JPEG files by using extreme learning machine
Published 2018“…This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. …”
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Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
Published 2006“…The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. …”
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A systematic review of recurrent neural network adoption in missing data imputation
Published 2025“…Missing data is a pervasive challenge in diverse datasets accross various domains. …”
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Deep reinforcement learning approaches for multi-objective problem in Recommender Systems
Published 2022“…Besides that, the experiment shows that agent which learning sequential data has earned lower precision by 17.57% and novelty by 4.68% compared to the agent that without learning sequential data, however, it achieved better diversity by 2.66%. …”
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