Multiple Imputation of Missing Data with Genetic Algorithm based Techniques

Patil, Dipak V.; Bichkar, Rajankumar S.

doi:10.5120/1537-140

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…For this reason, a brief test is to compare pieces of data -one with missing observations and the other without missing observations. On a t-test, if there is no mean difference between the two data units, we will expect that the data are MCAR [5]. Anything that is missing and sometimes because this form of missing facts is not often observed and the best manner to ignore these instances, for example: Water damage to paper forms due to flooding before it enters [1], [2] or in a survey, if we get 5% responses missing randomly, it is MCAR [6], [7].…”

Section: Missing Completely At Random (Mcar)mentioning

confidence: 99%

Missing value imputation Techniques: A Survey

Hameed

Ali²

2023

UHD J SCI TECH

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Numerous of information is being accumulated and placed away every day. Big quantity of misplaced areas in a dataset might be a large problem confronted through analysts due to the fact it could cause numerous issues in quantitative investigates. To handle such misplaced values, numerous methods were proposed. This paper offers a review on different techniques available for imputation of unknown information, such as median imputation, hot (cold) deck imputation, regression imputation, expectation maximization, help vector device imputation, multivariate imputation using chained equation, SICE method, reinforcement programming, non-parametric iterative imputation algorithms, and multilayer perceptrons. This paper also explores a few satisfactory choices of methods to estimate missing values to be used by different researchers on this discipline of study. Furthermore, it aims to assist them to discern out what approach is commonly used now, the overview may additionally provide a view of every technique alongside its blessings and limitations to take into consideration of future studies on this area of study. It can be taking into account as baseline to solutions the question which techniques were used and that is the maximum popular.

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Section: Missing Completely At Random (Mcar)mentioning

confidence: 99%

Missing value imputation Techniques: A Survey

Hameed

Ali²

2023

UHD J SCI TECH

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“…In [30], a GA is used to impute missing values in discrete features. In [27,22], GA-based imputation is also used for classification with missing values.…”

Section: Evolutionary Computation-based Imputationmentioning

confidence: 99%

A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data

et al. 2021

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Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist . In this work, a new imputation method for symbolic regression with incomplete data is proposed. The method aims to improve both the effectiveness and efficiency of imputing missing values for symbolic regression. This method is based on genetic programming (GP) and weighted K-nearest neighbors (KNN). It constructs GP-based models using other available features to predict the missing values of incomplete features. The instances used for constructing such models are selected using weighted KNN. The experimental results on real-world data sets show that the proposed method outperforms a number of state-of-the-art methods with respect to the imputation accuracy, the symbolic regression performance, and the imputation time.

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“…However, due to mechanical faults and changes in the system behavior, the collected traffic data often have corrupted or missing data points, bringing some error to analysis result [8,9] . The quality of traffic data not only deeply affects the analysis results of traffic flow operation, but also affects the efficiency of the traffic system operation [10][11][12]. For these reasons, increasingly more methods have been developed to measure and improve the traffic data quality in the past.…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

Liang

Qin

2019

Symmetry

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Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.Symmetry 2019, 11, 815 2 of 18 and model parameter selection can be shown. In Section 4, basic KNN approach will be introduced. In Section 5, a novel bidirectional data recovery approach is proposed, and the parameter setting process is presented. Section 6 discusses the experimental design and the results. Section 7 concludes the paper with a summary of bidirectional searching strategy and gives the suggestions for future work. Literature ReviewSeveral works focusing on recovering the abnormal data response to the data quality control strategy. These studies examine the effect of abnormal data and research the data recovery approach based on the historical aiming to improve data quality. Pushkar et al. [17] applied the catastrophe theory to establish the three-parameter sudden change surface of traffic flow to recover data and then proposed the speed estimation method. A nearest-neighbor imputation algorithm was developed and applied to interpolate the missing data based on the average value of historical data at the same time interval [18]. In addition, the factor approach estimated the missing data by using the mean value of the key factors selected from the historical set [19]. Troyanskaya et al. [20] investigated automated methods for estimating missing data to minimize the effect of incomplete data sets on analyses. Smith et al. [21] performed a preliminary analysis of several heuristic and statistical imputation techniques and declared the statistical techniques are more accurate. Chen et al. [22] proposed a linear regression algorithm to impute missing or bad traffic flow data and occupancy data using neighboring sensors data. Abdella et al. [23] proposed an integrated method combining the genetic algorithms with neural networks aiming at seeking the approximating missing data in a database. Tang et al.[24] developed a hybrid approach integrating Fuzzy C-Means-based (FCM) imputation method with a genetic algorithm (GA) to estimate the missing traffic volume data based on inductance loop sensor outputs. This approach ...

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Multiple Imputation of Missing Data with Genetic Algorithm based Techniques

Cited by 17 publications

References 18 publications

Missing value imputation Techniques: A Survey

Missing value imputation Techniques: A Survey

A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data

A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

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