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imputation method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters

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INTEGRATING MATERIALS AND MANUFACTURING INNOVATION 2022年第1期11卷 95-108页

作者： Deng, Jianxin Ye, Zhixing Shan, Lubao You, Dongdong Liu, Guangming Guangxi Univ Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530003 Peoples R China Guangxi Univ Sch Mech Engn Nanning 530003 Peoples R China South China Univ Technol Natl Engn Res Ctr Near Net Shape Forming Metall M Guangzhou 510640 Peoples R China

The development of a highly efficient methodology for establishing squeeze casting process parameters from past data is essential. However, designing squeeze casting process parameters based on past data is difficult when there are many missing values. Conventional missing data approaches are fraught with additional computational challenges when applied to high-dimensional multivariable missing data, especially material process data with correlation. As the relationship between material composition and process parameters has similar characteristics with that between users and information of interest, this paper proposes a method for missing data imputation based on a clustering-based collaborative filtering (ClubCF) algorithm to address this challenge. Data samples with and without missing values were divided into two groups. K-means clustering based on a canopy algorithm was applied to the data samples without missing values to obtain k subclass data, whose values were then selected to fill data samples with missing values via a collaborative filtering theory based on Pearson similarity user filling. The missing squeeze casting process parameters data of aluminum alloys were used to evaluate the method, and more comparative experiments were carried out to understand their performance and features. Two different indicators, including the mean absolute error and the standard deviation, were utilized to quantify the imputation performance, which was compared with those of three conventional methods (mean interpolation, regression interpolation, and the expectation maximization algorithm). The results indicate that the proposed approach is effective and outperforms conventional methods in processing high-dimensional correlated data.

关键词： Squeeze casting Data-driven materials manufacturing Missing data imputation method Clustering collaborative filtering Process data

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Model, properties and imputation method of missing SNP genotype data utilizing mutual information

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2009年第1期229卷 168-174页

作者： Wang, Ying Wan, Weiming Wang, Rui-Sheng Feng, Enmin Dalian Jiaotong Univ Sch Sci Dalian 116028 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China

Mutual information can be used as a measure for the association of a genetic marker or a combination of markers with the phenotype. In this paper, we study the imputation of missing genotype data. We first utilize joint mutual information to compute the dependence between SNP sites, then construct a mathematical model in order to find the two SNP sites having maximal dependence with missing SNP sites, and further study the properties of this model. Finally, an extension method to haplotype-based imputation is proposed to impute the missing values in genotype data. To verify our method, extensive experiments have been performed, and numerical results show that our method is superior to haplotype-based imputation methods. At the same time, numerical results also prove joint mutual information can better measure the dependence between SNP sites. According to experimental results, we also conclude that the dependence between the adjacent SNP sites is not necessarily strongest. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Mutual information imputation method Missing genotype data Missing SNP site Extension method

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Robust imputation method for missing values in microarray data

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BMC BIOINFORMATICS 2007年第s2期8卷 1-7页

作者： Yoon, Dankyu Lee, Eun-Kyung Park, Taesung Seoul Natl Univ Coll Nat Sci Dept Stat Seoul 151742 South Korea Seoul Natl Univ Interdisciplinary Program Bioinformat Seoul South Korea

Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis cannot be applied when the data have missing values. Numerous imputation algorithms have been proposed to estimate the missing values. In this study, we develop a robust least squares estimation with principal components (RLSP) method by extending the local least square imputation (LLSimpute) method. The basic idea of our method is to employ quantile regression to estimate the missing values, using the estimated principal components of a selected set of similar genes. Results: Using the normalized root mean squares error, the performance of the proposed method was evaluated and compared with other previously proposed imputation methods. The proposed RLSP method clearly outperformed the weighted k-nearest neighbors imputation (kNNimpute) method and LLSimpute method, and showed competitive results with Bayesian principal component analysis (BPCA) method. Conclusion: Adapting the principal components of the selected genes and employing the quantile regression model improved the robustness and accuracy of missing value imputation. Thus, the proposed RLSP method is, according to our empirical studies, more robust and accurate than the widely used kNNimpute and LLSimpute methods.

关键词： Quantile Regression Similar Gene imputation method Robust Regression Normalize Root Mean Square Error

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A homogenous forecast model based on the hybrid imputation method for forecasting national patent application numbers

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第14期83卷 41137-41169页

作者： Tsai, Ming-Chi I Shou Univ Dept Business Adm Kaohsiung 84001 Taiwan

Technological innovation is the key solution to promoting economic growth and improving quality of living. The number of patent applications in highly developed countries has become one of the standards for measuring national research and development. Therefore, this study reviews the literature to obtain 17 economic indicators that affect the number of patent applications in highly developed countries and collects data from the World Bank database. The collected data have many missing values. Hence, this study proposes a homogeneous model based on a hybrid imputation method for forecasting national patent applications. Specifically, this study uses k-means and self-organized maps to cluster the imputed dataset and applies an ensemble extra tree to forecast the number of patent applications of each cluster. The proposed hybrid imputation uses Google searching to obtain the variable value and then replaces the missing values of each country. If the Google searching provides no result for the country's attribute value, then apply the neighboring year data of the country to impute the missing values. For verification, this study uses listwise and pairwise methods to compare forecast performance with the proposed hybrid imputation method, and apply random forest, extra tree, Gaussian processes regression, radial basis function network, convolutional neural network, and long short-term memory to compare with the forecast performance of the proposed homogeneous model. The results show that the proposed homogeneous model with hybrid imputation have a better forecast performance in terms of the root mean square error, relative absolute error, and mean absolute error.

关键词： Patent applications Economic indicators Forecast model imputation method

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The Impact of Different Fold for Cross Validation of Missing Values imputation method on Hepatitis Dataset 14

The Impact of Different Fold for Cross Validation of Missing...

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14th International Conference on QiR (Quality in Research)

作者： Astuti, Tri Nugroho, Hanung Adi Adji, Teguh Bharata STMIK Amikom Dept Informat Engn Purwokerto Indonesia Univ Gadjah Mada Fac Engn Dept Elect Engn & Informat Technol Yogyakarta Indonesia

ISBN: (纸本)9781479965519

Hepatitis is a liver disease caused by hepatitis viruses. Nowadays, hepatitis is a global health problem, including in Indonesia. Chronic hepatitis can lead to cirrhosis and liver cancer, therefore early diagnosis is needed. Several research works on development of computer aided systems have been conducted to improve the diagnosis process of hepatitis disease. California Irvine (UCI) machine-learning repository provides hepatitis disease dataset which can be publicly accessed;however, the dataset contains many missing values. The existing of missing values in the dataset may affect the quality of the results analysis. Therefore, it needs to be conducted for handling the missing values. This paper analyses the performance of applying varied number of fold for cross validation of missing values imputation methods. The imputation method is combined with the feature selection method and machine-learning algorithm on the hepatitis dataset. The results that varied fold in k-fold cross validation which applied in the imputation method does not reveal significant advantages.

关键词： hepatitis missing values imputation method varied fold k-fold cross validation

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A novel imputation method for missing values in air pollutant time series data 6

A novel imputation method for missing values in air pollutan...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Pena, Mario Ortega, Patricia Orellana, Marcos Univ Azuay LIDI Cuenca Ecuador

ISBN: (纸本)9781728156668

Missing data is a widespread problem that studies in air quality have to deal with. The causes are varied, including sensor malfunctions and errors, power outages, computer system crashes, pollutant levels lower than detection limits, among others. Existing methods of data cleaning focus more on anomaly detection paying less attention to repairing them. Anomaly detection is a common approach used for filtering out dirty data. However, this approach could still result in unreliable data due to the incomplete data resulting from this process. This article presents an approach for repairing continuous sections of missing values in time series of air pollutant data. This study considers two regularized methods, Lasso and Ridge regression, to determine the number of data points forward and backward needed to estimate the value of a missing data point. Lasso presents superiority in front of Ridge regression to generate models for data imputation. The performance of the method was evaluated using the correlation coefficient (R-2), the index agreement (d), the mean absolute error (MAE), and root mean square error (RMSE). The proposed imputation method exhibited good results in terms of accuracy and precision in gaps of different lengths. This study proved that the proposed imputation method is useful to impute accurately missing values in air pollution contaminant datasets, and it has the potential to be applied to any dataset configured as a time series.

关键词： Missing values imputation method Time series

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A novel graph-based missing values imputation method for industrial lubricant data

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COMPUTERS IN INDUSTRY 2023年第1期150卷

作者： Jeong, Soohwan Joo, Chonghyo Lim, Jongkoo Cho, Hyungtae Lim, Sungsu Kim, Junghwan Chungnam Natl Univ Dept Comp Sci Engn 99 Daehak Ro Daejeon South Korea Korea Inst Ind Technol Green Mat & Proc R&D Grp Ulsan 44413 South Korea Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea GS Caltex Corp Res & Dev Ctr 359 Expo Ro Daejeon 34122 South Korea

Missing values are unavoidable in lubricant formulation data in the chemical industry owing to the complexity of lubricant manufacturing. Therefore, imputing missing values using statistical analysis and data mining is essential to obtain meaningful information such as correlations and patterns. Traditional methods, such as random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), and deep neural networks (DNNs), have been employed for imputing missing values. However, these traditional methods neglect the latent structure because they only consider the feature information of the data. To this end, this study proposed a novel graph-based imputation method (GBIM) considering the feature information and the relations between data points to improve model performance. The proposed GBIM expresses the relation between each data point via a graph by consulting with dependency modeling and imputes missing values using a graph convolutional network (GCN). Experiments were performed for four physical properties in a lubricant formulation dataset. The results using GBIM were compared with those of traditional imputation methods (RF, k-NN, SVM, and DNN) by considering missing rates at 5% intervals from 5% to 50%. GBIM achieved 4-7% higher imputation accuracy than the other methods. The proposed GBIM can be applied in various industries as a powerful method for imputing missing values.

关键词： Lubricant formulation Missing values imputation method Graph Convolutional Network

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Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

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BMC BIOINFORMATICS 2008年第1期9卷 12-12页

作者： Brock, Guy N. Shaffer, John R. Blakesley, Richard E. Lotz, Meredith J. Tseng, George C. Univ Pittsburgh Grad Sch Publ Hlth Dept Human Genet Pittsburgh PA 15261 USA Univ Louisville Sch Publ Hlth & Informat Sci Dept Bioinformat & Biostat Louisville KY 40292 USA Univ Pittsburgh Grad Sch Publ Hlth Dept Biostat Pittsburgh PA 15261 USA Univ Pittsburgh Sch Med Dept Computat Biol Pittsburgh PA 15213 USA

Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. Results: We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. Conclusion: Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroar

关键词： Partial Little Square Ordinary Little Square Singular Value Decomposition imputation method Entropy Measure

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An Effective imputation method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

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REMOTE SENSING 2023年第13期15卷 3374-3374页

作者： Gouran, Payam Nadimi-Shahraki, Mohammad H. Rahmani, Amir Masoud Mirjalili, Seyedali Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran Islamic Azad Univ Fac Comp Engn Res Sci Branch Tehran *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Douliou 123 Univ RdSect 3 Yunlin 64002 Taiwan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane 4006 Australia

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C-1 horizontal ellipsis C-n classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method's accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method's accuracy.

关键词： intelligent traffic control system intersection traffic loop detector missed-volume data multi-class imputation method

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A road network traffic flow data imputation method based on the fusion of spatiotemporal features and adversarial networks

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Zhang, Yaofang Jian, Chen Fu, Zhiyan Chongqing Jiaotong Univ Sch Traff & Transportat Chongqing Peoples R China Chongqing Univ Educ Sch Econ & Business Adm Chongqing Peoples R China

In response to the problem of missing traffic flow data on highways, to solve the problem of insufficient mining of traffic flow characteristics using existing spatiotemporal correlation repair methods, a missing data repair method based on spatiotemporal fusion adversarial network is proposed based on analyzing the spatiotemporal characteristics of traffic flow, this method utilizes the fusion of GRU and GCN to capture the fine-grained spatiotemporal relationships of traffic flow, optimizes the generator and discriminator of the adversarial network, and achieves accurate repair of missing data. Experiments based on real data have shown that under various missing modes and rates, the model can learn the topological relationships of the road network, capture the temporal regularity and spatiotemporal correlation in the data, and effectively impute missing data.

关键词： Intelligent transportation spatiotemporal characteristics of traffic data adversarial network missing data imputation method

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