Relevant bibliographies by topics / KNN imputation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'KNN imputation'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'KNN imputation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "KNN imputation"

1

Gautam, Ramu, and Shahram Latifi. "COMPARISON OF SIMPLE MISSING DATA IMPUTATION TECHNIQUES FOR NUMERICAL AND CATEGORICAL DATASETS." Journal of Research in Engineering and Applied Sciences 8, no. 1 (2023): 468–75. http://dx.doi.org/10.46565/jreas.202381468-475.

Full text
Abstract:
Almost every dataset has missing data. The common reasons are sensor error, equipment malfunction, human error, or translation loss. We study the efficacy of statistical (mean, median, mode) and machine learning based (k-nearest neighbors) imputation methods in accurately imputing missing data in numerical datasets with data missing not at random (MNAR) and data missing completely at random (MCAR) as well as categorical datasets. Imputed datasets are used to make prediction on the test set and Mean squared error (MSE) in prediction is used as the measure of performance of the imputation. Mean
APA, Harvard, Vancouver, ISO, and other styles
2

Mamat, Naeimah, and Siti Fatin Mohd Razali. "Comparisons of Various Imputation Methods for Incomplete Water Quality Data: A Case Study of The Langat River, Malaysia." Jurnal Kejuruteraan 35, no. 1 (2023): 191–201. http://dx.doi.org/10.17576/jkukm-2023-35(1)-18.

Full text
Abstract:
In this study, the ability of numerous statistical and machine learning models to impute water quality data was investigated at three monitoring stations along the Langat River in Malaysia. Inconsistencies in the percentage of missing data between monitoring stations (varying from 20 percent (moderate) to over 50 percent (high)) represent the greatest obstacle of the study. The main objective was to select the best method for imputation and compare whether there are differences between the methods used by the different stations. The paper focuses on different imputation methods such as Multipl
APA, Harvard, Vancouver, ISO, and other styles
3

Abidin, Nadzurah Zainal, and Amelia Ritahani Ismail. "An improved K-Nearest neighbour with grasshopper optimization algorithm for imputation of missing data." International Journal of Advances in Intelligent Informatics 7, no. 3 (2021): 304. http://dx.doi.org/10.26555/ijain.v7i3.696.

Full text
Abstract:
K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. However, despite the favorable points, KNN still imposes undesirable circumstances. KNN suffers from high time complexity, choosing the right k, and different functions. Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algor
APA, Harvard, Vancouver, ISO, and other styles
4

Qin, Yongsong, Shichao Zhang, and Chengqi Zhang. "Combining kNN Imputation and Bootstrap Calibrated." International Journal of Data Warehousing and Mining 6, no. 4 (2010): 61–73. http://dx.doi.org/10.4018/jdwm.2010100104.

Full text
Abstract:
The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This paper studies a new estimation for missing (or incomplete) data that is a combination of the kNN imputation and bootstrap calibrated EL (Empirical Likelihood). The combination not only releases the burden of seeking a mathematical valid asymptotic theory for the kNN imputati
APA, Harvard, Vancouver, ISO, and other styles
5

Murad Ali, Khan. "Enhancing Material Property Predictions through Optimized KNN Imputation and Deep Neural Network Modeling." IgMin Research 2, no. 6 (2024): 425–31. http://dx.doi.org/10.61927/igmin197.

Full text
Abstract:
In materials science, the integrity and completeness of datasets are critical for robust predictive modeling. Unfortunately, material datasets frequently contain missing values due to factors such as measurement errors, data non-availability, or experimental limitations, which can significantly undermine the accuracy of property predictions. To tackle this challenge, we introduce an optimized K-Nearest Neighbors (KNN) imputation method, augmented with Deep Neural Network (DNN) modeling, to enhance the accuracy of predicting material properties. Our study compares the performance of our Enhance
APA, Harvard, Vancouver, ISO, and other styles
6

Hina, Ayub, and Jamil Harun. "Enhancing Missing Values Imputation through Transformer-Based Predictive Modeling." IgMin Research 2, no. 1 (2024): 025–31. http://dx.doi.org/10.61927/igmin140.

Full text
Abstract:
This paper tackles the vital issue of missing value imputation in data preprocessing, where traditional techniques like zero, mean, and KNN imputation fall short in capturing intricate data relationships. This often results in suboptimal outcomes, and discarding records with missing values leads to significant information loss. Our innovative approach leverages advanced transformer models renowned for handling sequential data. The proposed predictive framework trains a transformer model to predict missing values, yielding a marked improvement in imputation accuracy. Comparative analysis agains
APA, Harvard, Vancouver, ISO, and other styles
7

Manyol, Moïse, Samuel Eke, Alphonse J. M. Massoma, Alain Biboum, and Ruben Mouangue. "Preprocessing Approach for Power Transformer Maintenance Data Mining Based on k-Nearest Neighbor Completion and Principal Component Analysis." International Transactions on Electrical Energy Systems 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/8546588.

Full text
Abstract:
The accuracy of a knowledge extraction algorithm in a large database depends on the quality of the data preprocessing and the methods used. The massive amounts of data that we collect every day are putting storage capacity at a premium. In reality, many databases are characterized by attributes with outliers, redundant, and even more missing values. Missing data and outliers are ubiquitous in our databases, and imputation techniques will help us mitigate their influence. To solve this problem, as well as the problem of data size, this paper proposes a data preprocessing approach based on the k
APA, Harvard, Vancouver, ISO, and other styles
8

Kim, Sung Won, and Young Il Kim. "A Data Imputation Approach for Missing Power Consumption Measurements in Water-Cooled Centrifugal Chillers." Energies 18, no. 11 (2025): 2779. https://doi.org/10.3390/en18112779.

Full text
Abstract:
In the process of collecting operational data for the performance analysis of water-cooled centrifugal chillers, missing values are inevitable due to various factors such as sensor errors, data transmission failures, and failure of the measurement system. When a substantial amount of missing data is present, the reliability of data analysis decreases, leading to potential distortions in the results. To address this issue, it is necessary to either minimize missing occurrences by utilizing high-precision measurement equipment or apply reliable imputation techniques to compensate for missing val
APA, Harvard, Vancouver, ISO, and other styles
9

Syauqi, Rofiq Muhammad, Puspita Nurul Sabrina, and Irma Santikarama. "K-Means Clustering with KNN and Mean Imputation on CPU Benchmark Compilation Data." Journal of Applied Informatics and Computing 7, no. 2 (2023): 231–39. http://dx.doi.org/10.30871/jaic.v7i2.6491.

Full text
Abstract:
In the rapidly evolving digital age, data is becoming a valuable source for decision-making and analysis. Clustering, as an important technique in data analysis, has a key role in organizing and understanding complex datasets. One of the effective clustering algorithms is k-means. However, this algorithm is prone to the problem of missing values, which can significantly affect the quality of the resulting clusters. To overcome this challenge, imputation methods are used, including mean imputation and K-Nearest Neighbor (KNN) imputation. This study aims to analyze the impact of imputation metho
APA, Harvard, Vancouver, ISO, and other styles
10

Du, Wenyou, Yichen Wang, Guanglei Meng, and Yuming Guo. "Privacy-Preserving Vertical Federated KNN Feature Imputation Method." Electronics 13, no. 2 (2024): 381. http://dx.doi.org/10.3390/electronics13020381.

Full text
Abstract:
Federated learning stands as a pivotal component in the construction of data infrastructure. It significantly fortifies the safety and reliability of data circulation links, facilitating credible sharing and openness among diverse subjects. The presence of missing data poses a pervasive and challenging issue in the implementation of federated learning. Current research on imputation missing values predominantly concentrates on centralized methods and horizontal federation scenarios. However, there is a notable absence of exploration in the context of vertical federated application scenarios. I
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "KNN imputation"

1

Santos, Fábio dos. "Algoritmo kNN na imputação de dados de espectros de massa do tipo MALDI-TOF: uma análise da influência da imputação com kNN sobre o desempenho de classificadores logísticos para identificação de bactérias." Universidade Estadual de Ponta Grossa, 2018. http://tede2.uepg.br/jspui/handle/prefix/2665.

Full text
Abstract:
Submitted by Angela Maria de Oliveira (amolivei@uepg.br) on 2018-11-06T17:08:39Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Fábio dos Santos.pdf: 1456053 bytes, checksum: 5ee15a88a68aaef87a46a8f42f816e32 (MD5)<br>Made available in DSpace on 2018-11-06T17:08:39Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Fábio dos Santos.pdf: 1456053 bytes, checksum: 5ee15a88a68aaef87a46a8f42f816e32 (MD5) Previous issue date: 2018-09-14<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superi
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "KNN imputation"

1

Yang, Yuzhao, Jérôme Darmont, Franck Ravat, and Olivier Teste. "Dimensional Data KNN-Based Imputation." In Advances in Databases and Information Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15740-0_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Veena, E. V., and K. P. Pushpalatha. "Enhanced KNN Imputation for Missing Data." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1758-6_48.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Feng, Yifan, Yutong Ai, and Hao Jiang. "LLE Based K-Nearest Neighbor Smoothing for scRNA-Seq Data Imputation." In Financial Mathematics and Fintech. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2366-3_11.

Full text
Abstract:
AbstractThe single-cell RNA sequencing (scRNA-seq) technique allows single cell level of gene expression measurements, but the scRNA-seq data often contain missing values, with a large proportion caused by technical defects failing to detect gene expressions, which is called dropout event. The dropout issue poses a great challenge for scRNA-seq data analysis. In this chapter, we introduce a method based on KNN-smoothing: LLE-KNN-smoothing to impute the dropout values in scRNA-seq data and show that the LLE-KNN-smoothing greatly improves the recovery of gene expression in cells and shows better
APA, Harvard, Vancouver, ISO, and other styles
4

Lalande, Florian, and Kenji Doya. "Numerical Data Imputation: Choose kNN over Deep Learning." In Similarity Search and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17849-8_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gond, Vikesh Kumar, Aditya Dubey, Akhtar Rasool, and Nilay Khare. "Missing Value Imputation Using Weighted KNN and Genetic Algorithm." In ICT Analysis and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5224-1_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Poloczek, Jendrik, Nils André Treiber, and Oliver Kramer. "KNN Regression as Geo-Imputation Method for Spatio-Temporal Wind Data." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07995-0_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Al-Helali, Baligh, Qi Chen, Bing Xue, and Mengjie Zhang. "A Hybrid GP-KNN Imputation for Symbolic Regression with Missing Values." In AI 2018: Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Schmitt, Susanne, and Franz Rothlauf. "Comparison of Imputation Methods for Categorical Real-World Prostate Cancer Data with Natural Order." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240780.

Full text
Abstract:
Missing values (NA) often occur in cancer research, which may be due to reasons such as data protection, data loss, or missing follow-up data. Such incomplete patient information can have an impact on prediction models and other data analyses. Imputation methods are a tool for dealing with NA. Cancer data is often presented in an ordered categorical form, such as tumour grading and staging, which requires special methods. This work compares mode imputation, k nearest neighbour (knn) imputation, and, in the context of Multiple Imputation by Chained Equations (MICE), logistic regression model wi
APA, Harvard, Vancouver, ISO, and other styles
9

Joshi, Hemlata, A. Vijayalakshmi, and Sneha Maria George. "Unraveling the Complexity of Thyroid Cancer Prediction." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5288-5.ch014.

Full text
Abstract:
Despite being relatively rare, thyroid cancer is being identified more often as a result of improved awareness and detection. Even if it has a high survival rate, it is crucial to comprehend its forms, risk factors, and therapies. Better results and prompt intervention are made possible by the early detection of thyroid cellular alterations made possible by evolving machine learning (ML) techniques. The USA Cancer Data Access System's Thyroid Cancer Factor Data, gathered from patient questionnaires, are used in this study. Missing values and imbalance in the dataset are addressed using resampl
APA, Harvard, Vancouver, ISO, and other styles
10

Aggarwal, Ritu, and Suneet Kumar. "Missing Value Imputation and Estimation Methods for Arrhythmia Feature Selection Classification Using Machine Learning Algorithms." In Machine Learning Methods for Engineering Application Development. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9879815079180122010013.

Full text
Abstract:
&amp;nbsp;Electrocardiogram signal analysis is very difficult to classify cardiac arrhythmia using machine learning methods. The ECG datasets normally come with multiple missing values. The reason for the missing values is the faults or distortion. When performing data mining, missing value imputation is the biggest task for data preprocessing. This problem could arise due to incomplete medical datasets if the incomplete missing values and cases were removed from the original database. To produce a good quality dataset for better analyzing the clinical trials, the suitable missing value imputa
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "KNN imputation"

1

Abnane, Ibtissam, Mohamed Hosni, Ali Idri, and Alain Abran. "Analogy Software Effort Estimation Using Ensemble KNN Imputation." In 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2019. http://dx.doi.org/10.1109/seaa.2019.00044.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Makkar, Gaurav, and Farhan Gandhi. "Deep Learning Based Framework for Multicopter Sensor Data Imputation." In Vertical Flight Society 79th Annual Forum & Technology Display. The Vertical Flight Society, 2023. http://dx.doi.org/10.4050/f-0079-2023-18098.

Full text
Abstract:
The study presented in this paper introduces a novel approach for imputing missing sensor data in multicopters, which enables enhanced safety and reliability by leveraging the multitude of sensors on these aerial vehicles. The proposed approach is based on two deep learning techniques, namely Autoencoders (AE) and Long Short-Term Memory (LSTM) networks. The effectiveness of this approach is evaluated using flight test data from a 2.5 kg hexacopter, and three different scenarios of missing data are considered. To validate the performance of the proposed approach, it is compared against two comm
APA, Harvard, Vancouver, ISO, and other styles
3

Al-Zoubi, Ahmad, Konstantinos Tatas, and Costas Kyriacou. "Design space exploration of the KNN imputation on FPGA." In 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2018. http://dx.doi.org/10.1109/mocast.2018.8376606.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Capariño, Elenita Tabora. "Improving kNN Classification Performance with Correlated Attribute Imputation Mechanism." In ICIIT 2024: 2024 9th International Conference on Intelligent Information Technology. ACM, 2024. http://dx.doi.org/10.1145/3654522.3654547.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Oehmcke, Stefan, Oliver Zielinski, and Oliver Kramer. "kNN ensembles with penalized DTW for multivariate time series imputation." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727549.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Keerin, Phimmarin, Werasak Kurutach, and Tossapon Boongoen. "Cluster-based KNN missing value imputation for DNA microarray data." In 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2012. http://dx.doi.org/10.1109/icsmc.2012.6377764.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Giraldo Delgado, Juan Camilo, Nursulu Kuzhagaliyeva, Inna Gorbatenko, and Mani Sarathy. "Predicting Vehicle Engine Performance: Assessment of Machine Learning Techniques and Data Imputation." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2016.

Full text
Abstract:
&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective te
APA, Harvard, Vancouver, ISO, and other styles
8

Zhu, Ming, and Xingbing Cheng. "Iterative KNN imputation based on GRA for missing values in TPLMS." In 2015 4th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2015. http://dx.doi.org/10.1109/iccsnt.2015.7490714.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Nugroho, Heru, Nugraha Priya Utama, and Kridanto Surendro. "kNN Imputation Versus Mean Imputation for Handling Missing Data on Vulnerability Index in Dealing with Covid-19 in Indonesia." In ICSCA 2023: 2023 12th International Conference on Software and Computer Applications. ACM, 2023. http://dx.doi.org/10.1145/3587828.3587832.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Moatadid, Ismail, Ibtissam Abnane, and Ali Idri. "Comparing Ensemble and Single Classifiers Using KNN Imputation for Incomplete Heart Disease Datasets." In 15th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0012208300003598.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'KNN imputation' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography