K-NN Classifier Performs Better Than K-Means Clustering in Missing Value Imputation

Malarvizhi, M.

doi:10.9790/0661-0651215

Cited by 28 publications

(16 citation statements)

References 2 publications

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“…Missing data is a general weakness that can influence the consequences of the prediction system to be ineffective [1][2][3]. Ignoring the missing data has an impact on the results of the analysis [4][5][6][7][8][9], learning outcomes, predictive results [10] and potentially weakens the validity of the results and conclusions [8,9] and leads to estimation of biased parameters [7,[11][12][13][14]. Prediction and classification are the principle obligations required in many areas and spaces that expect admittance to finish and accurate data [15].…”

Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.

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Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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“…The weakness of KNNI method is to find the lost value, KNN imputation algorithm will search through all the dataset. However, KNN imputation method is a method that is good enough for a missing value imputation [1,4,5,[9][10][11]13].…”

Section: Knn Imputationmentioning

confidence: 99%

“…K-Nearest Neighbour method applies KNNI algorithms which are commonly used in the classification process to handle missing values. KNNI method can find the value of closest neighborhood data that has missing value on attributes in the amount of k [9][10][11][12][13] System/component data is retrieved from the univariate of maintenance historical data of secondary coolant system RSG GAS (PA), ie: (PA01/CP001, PA01/CR001, PA01/AH001, PA01/BT001, PA02/AP002, PA02/BT001). Matlab was used to build code of handling missing values.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Missing Values Handling Method for Evaluating the System/Component Maintenance Historical Data

Hartini

2017

Tri Dasa Mega

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IMPLEMENTATION OF MISSING VALUES HANDLING METHOD FOR EVALUATING THE SYSTEM/COMPONENT MAINTENANCE HISTORICAL DATA.Missing values are problems in data evaluation. Missing values analysis can resolve the problem of incomplete data that is not stored properly. The missing data can reduce the precision of calculation, since the amount of information is incomplete. The purpose of this study is to implement missing values handling method for systems/components maintenance historical data evaluation in RSG GAS. Statistical methods, such as listwise deletion and mean substitution, and machine learning (KNNI), were used to determine the missing data that correspond to the systems/components maintenance historical data. Mean substitution and KNNI methods were chosen since those methods do not require the formation of predictive models for each item which is experiencing missing data. Implementation of missing data analysis on systems/components maintenance data using KNNI method results in the smallest RMSE value. The result shows that KNNI method is the best method to handle missing value compared with listwise deletion or mean substitution.

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“…R. Malaryizhi et al recruit K-NN classifier performs superior than K-means clustering in missing value imputation. [9]. Phimmarin Keerin proposed a new methodology CKNN (cluster based K-NN) to impute missing values in microarray data [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Preprocessing Techniques for NID3R

Suthar¹

2017

ijecs

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Abstract-Stream data mining is the process of excerpting knowledge structure from large, continuous data. For stream data, various techniques are proposed for preparing the data for data mining task. In recent years stream data have become a growing area for the researcher, but there are many issues occurring in classifying these data due to erroneous and noisy data. Change of trend in the data periodically produces major challenge for data miners. Column oriented data storage has shown fast access to data used in data mining. Various missing value replacement algorithms are implemented using MonetDB. This research also concentrates on incremental missing value replacement for stream data. The proposed method generates the value for the missing data considering the data type and data distribution. It also considers the concept drift in the data stream. The method will applied to different datasets and promising results will be expected

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K-NN Classifier Performs Better Than K-Means Clustering in Missing Value Imputation

Cited by 28 publications

References 2 publications

Class Center-Based Firefly Algorithm for Handling Missing Data

Class Center-Based Firefly Algorithm for Handling Missing Data

Implementation of Missing Values Handling Method for Evaluating the System/Component Maintenance Historical Data

Novel Preprocessing Techniques for NID3R

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