Futuristic Prediction of Missing Value Imputation Methods Using Extended ANN

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.

Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

Smartic: A smart tool for Big Data analytics and IoT

The Internet of Things (IoT) is leading the physical and digital world of technology to converge. Real-time and massive scale connections produce a large amount of versatile data, where Big Data comes into the picture. Big Data refers to large, diverse sets of information with dimensions that go beyond the capabilities of widely used database management systems, or standard data processing software tools to manage within a given limit. Almost every big dataset is dirty and may contain missing data, mistyping, inaccuracies, and many more issues that impact Big Data analytics performances. One of the biggest challenges in Big Data analytics is to discover and repair dirty data; failure to do this can lead to inaccurate analytics results and unpredictable conclusions. We experimented with different missing value imputation techniques and compared machine learning (ML) model performances with different imputation methods. We propose a hybrid model for missing value imputation combining ML and sample-based statistical techniques. Furthermore, we continued with the best missing value inputted dataset, chosen based on ML model performance for feature engineering and hyperparameter tuning. We used k-means clustering and principal component analysis. Accuracy, the evaluated outcome, improved dramatically and proved that the XGBoost model gives very high accuracy at around 0.125 root mean squared logarithmic error (RMSLE). To overcome overfitting, we used K-fold cross-validation.

Improvement of Accuracy and Handling of Missing Value Data in the Naive Bayes Kernel Algorithm

The lost impact on the research process, can be serious in classifying results leading to biased parameter estimates, statistical information, decreased quality, increased standard error, and weak generalization of the findings. In this paper, we discuss the problems that exist in one of the algorithms, namely the Naive Bayes Kernel algorithm. The Naive Bayes kernel algorithm has the disadvantage of not being able to process data with the mission value. Therefore, in order to process missing value data, there is one method that we propose to overcome, namely using the mean imputation method. The data we use is public data from UCI, namely the HCV (Hepatisis C Virus) dataset. The input method used to correct the missing data so that it can be filled with the average value of the existing data. Before the imputation process means, the dataset uses yahoo bootstrap first. The data that has been corrected using the mean imputation method has just been processed using the Naive Bayes Kernel Algorithm. From the results of the research tests that have been carried out, it can be obtained an accuracy value of 96.05% and the speed of the data computing process with 1 second.

Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour

For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required. The gene sample value can be missing due to hardware failure, software failure, or manual mistakes. The missing data in gene expression research dramatically affects the analysis of the collected data. Consequently, this has become a critical problem that requires an efficient imputation algorithm to resolve the issue. This paper proposed a technique considering the local similarity structure that predicts the missing data using clustering and top K nearest neighbor approaches for imputing the missing value. A similarity-based spectral clustering approach is used that is combined with the K-means. The spectral clustering parameters, cluster size, and weighting factors are optimized, and after that, missing values are predicted. For imputing each cluster’s missing value, the top K nearest neighbor approach utilizes the concept of weighted distance. The evaluation is carried out on numerous datasets from a variety of biological areas, with experimentally inserted missing values varying from 5 to 25%. Experimental results prove that the proposed imputation technique makes accurate predictions as compared to other imputation procedures. In this paper, for performing the imputation experiments, microarray gene expression datasets consisting of information of different cancers and tumors are considered. The main contribution of this research states that local similarity-based techniques can be used for imputation even when the dataset has varying dimensionality and characteristics.

Missing Value Imputation of Time-Series Air-Quality Data via Deep Neural Networks

To prevent severe air pollution, it is important to analyze time-series air quality data, but this is often challenging as the time-series data is usually partially missing, especially when it is collected from multiple locations simultaneously. To solve this problem, various deep-learning-based missing value imputation models have been proposed. However, often they are barely interpretable, which makes it difficult to analyze the imputed data. Thus, we propose a novel deep learning-based imputation model that achieves high interpretability as well as shows great performance in missing value imputation for spatio-temporal data. We verify the effectiveness of our method through quantitative and qualitative results on a publicly available air-quality dataset.