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22 articles for “missing values”
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Research ArticleOpen accessPhrase in the titleParameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit ModelsSuwash Silwal, Xiao ZhangNominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. Due to the ubiquitousness of missing data, it is inevitable to analyze nominal data with missing values. As is well known, statistical methods for analyzing nominal variables are quite limited. The Multinomial Probit (MNP) model has been an essential tool for analyzing nominal categorical data, but … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 23 May 2026 · 1,804 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleMissing Values Treatment and Feature Reduction Analysis to Enhance ClassificationD. Muralidharan, K. Renuka, Mulagala Jaswant et al.Datasets may have large number of features which makes it hard and time consuming to classify. Additionally, they may have irrelevant and noise features too with missing values. The missing values should be treated in a proper way so that the classifier accuracy can be improved. There is also a need to reduce features and select only the features necessary … …Journal of Computer Science, Vol. 16, Issue 2 · 20 Feb 2020 · 8,114 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleOn the Comparison of Methods of Estimating Missing Values in Rectangular Lattice DesignsAbimibola V. Oladugba, Emmanuel O. Ossai, Tobias E. UgahMissing values occur in almost all research which lead to ambiguity in data analysis. It becomes necessary that appropriate consideration is made in order to provide an efficient and valid analysis. Researchers have developed and compared a variety of methods of estimating missing values in experimental designs; however, no research work has derived and compared methods of estimating missing values, … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 11 Oct 2018 · 7,044 accessesView PDFCite
Research ArticleOpen accessEvery word in the titlePredicting Missing Attribute Values Using k-Means ClusteringNambiraj Suguna, Keppana Gowder ThanushkodiProblem statement: Predicting the value for missing attributes is an important data preprocessing problem in data mining and knowledge discovery tasks. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. When the dataset has minimum number of missing attribute values then we … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,873 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPiecewise Positivity Preserving Cubic Bezier Interpolation for Estimating Solar Radiation Missing Value in Penang, MalaysiaAzizan Saaban, Mohd Lutfi Zainudin, Mohd Nazari Abu BakarComplete solar radiation data is important in variety of disciplines such as solar radiation development system or solar project which is strongly affected by hourly variation of the solar radiation. However, the available data may contain missing data for several days in the absence of measurement due to several factors such as instrument failure and human errors. In this study, … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 28 Dec 2016 · 7,119 accessesView PDFCite
Research ArticleOpen accessIn the keywordsImproving Accuracy and Coverage of Data Mining Systems that are Built from Noisy Datasets: A New ModelLuai A. Al ShalabiProblem statement: Noise within datasets has to be dealt with under most circumstances. This noise includes misclassified data or information as well as missing data or information. Simple human error is considered as misclassification. These errors will decrease the accuracy of the data mining system so it will not be likely to be used. The objective was to propose an … …Journal of Computer Science, Vol. 5, Issue 2 · 28 Feb 2009 · 7,820 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModified Human Development Index using Data Envelopment Analysis ApproachYasmine Refai Salama, Ramadan Hamed, Mahmoud RashwanComposite Indicator is considered themathematical aggregation which has wide usage for monitoring performances,conducting benchmarks, analyzing policies, and communicating publicly. HumanDevelopment Index (HDI) is the most popular index which measures humandevelopment through average achievement in its main dimensions: Health status,education status, and living standard but it is faced with several critiques,positive and negative. Moreover, HDI was tested to have a positive … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 26 Sep 2022 · 6,088 accessesView PDFCite
Research ArticleOpen accessIn the abstractPandemic Analysis and Preparedness Using a Three Layer Machine Learning ApproachArtika Singh, Manisha JailiaOutbreak analysis provides research-based information and data to enable scientific decision-making to manage an effective response to a pandemic. Pandemic analysis has its own set of challenges, such as limited data, the need for real time analysis, and the complexity of outbreak patterns. This study proposes a hybrid approach to manage such a spread through a 3-layer machine learning-based outbreak … …Journal of Computer Science, Vol. 22, Issue 11 · 15 Oct 2026 · 614 accessesView PDFCite
Research ArticleOpen accessIn the abstractEvaluation of Machine Learning Models to Predict Student Academic Performance Using Structured Educational DataHardik Ishwarbhai Patel, Dharmendra PatelThis study analyses the use of machine learning for predicting the academic performance of students using their academic information from the institution, combined with socio-economic information that comes from outside sources. The collection of information is done using structured questionnaires as well as through data extraction from the Student Information System (SIS). To increase the reliability of models built, a … …Journal of Computer Science, Vol. 22, Issue 5 · 26 May 2026 · 1,419 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalyzing Public Sentiment on Demonetization Using SVM: A Machine Learning ApproachKaliappan M, Guruprakash B, Rajalakshmi et al.The Indian economy experienced significant disruption following the implementation of demonetization, a policy initiative aimed at eliminating black money, controlling inflation, and promoting financial inclusion. However, this currency ban generated widespread debate and polarized public opinion. This study analyzes public sentiment toward demonetization using social media data, specifically Twitter posts characterized by mixed sentiments, sarcasm, and nuanced linguistic expressions. We … …Journal of Computer Science, Vol. 21, Issue 11 · 18 Dec 2025 · 2,235 accessesView PDFCite