An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data

Ozan, Ezgi Can; Riabchenko, Ekaterina; Kıranyaz, Serkan; Gabbouj, Moncef

doi:10.1007/978-3-319-46349-0_34

Cited by 11 publications

(7 citation statements)

References 3 publications

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“…Condek et al [12] applied median imputation address missing data and concluded that RF can be used as a cost function providing better results than the naïve approaches of checking every truck or no truck until failure. Ozan et al [13] introduced an optimized k-NN approach to handle missing values and created a tailored k-NN model using a specified HEIM distance. Biteus and Lindgren [14] removed attributes with more than 10% missing values and applied mean imputation to the remaining attributes.…”

Section: Discussionmentioning

confidence: 99%

“…In terms of publication time, it can be divided into two scopes. Previous studies from 2016 to 2019 March [10][11][12][13][14][15][16] have used many dedicated methods of data imputation and machine learning algorithms to solve the high-class imbalance and missing data problems [17][18][19][20][21][22][23][24]. However, these studies have focused only on the training set.…”

Section: Related Workmentioning

confidence: 99%

“…Random forest (RF) [12,13,29,31,38] is an ensemble learning algorithm that involves a collection of decision trees (i.e., classifiers). The decision trees are independent identically distributed random vectors.…”

Section: Random Forests (Rfs)mentioning

confidence: 99%

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Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

2020

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Missing data are common in industrial sensor readings owing to system updates and unequal radio-frequency periods. Existing methods addressing missing data through imputation may not always be appropriate. This study presented a sorted missing percentages technique for filtering attributes when building machine learning classification models using sensor readings with missing data. Signal detection theory was employed to evaluate the distinguishing ability of resulting models. To evaluate its performance, the proposed technique was applied to a publicly available air pressure system dataset, which then was used to build several classifiers. The experimental results indicated that the proposed technique allowed a logistic regression model to achieve the best accuracy score (99.56%) and a better distinguishing ability (response bias of 0.0013, adjusted response bias of 0.0044, and decision criterion of −1.8994) compared with the methods applied to the same dataset and reported in papers published between 2016 and 2019 March on binary classification, wherein attributes with more than 20% of missing data were filtered out. The proposed technique is suitable for industrial sensor data analysis and can be applied to the scenarios dealing with missing data owing to unequal radio-frequency periods or a system being updated with new fields.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

2020

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“…This data is heavily imbalanced (74 observations for the "diseased trees" class vs 4,265 observations for "other land cover" class). APS Failure data set was collected from heavy scania trucks in everyday usage [15]. The task is to diagnose whether a truck failure is caused by a component of the air pressure system (APS) from 170 anonymous factors.…”

Section: Methodsmentioning

confidence: 99%

Averaging versus voting: A comparative study of strategies for distributed classification

Wang¹,

Xu²,

Wu³

2020

Mathematical Foundations of Computing

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In this paper we proposed two strategies, averaging and voting, to implement distributed classification via the divide and conquer approach. When a data set is too big to be processed by one processor or is naturally stored in different locations, the method partitions the whole data into multiple subsets randomly or according to their locations. Then a base classification algorithm is applied to each subset to produce a local classification model. Finally, averaging or voting is used to couple the local models together to produce the final classification model. We performed thorough empirical studies to compare the two strategies. The results show that averaging is more effective in most scenarios.

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“…Ozan E.C et al [4] discussed an optimized k-NN classifier to estimate missing data on this imbalanced dataset. They handled the missing value problem by using KNN imputation.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

Rafsunjani¹,

Safa²,

Imran³

et al. 2019

IJITCS

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The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking and gear changing. The APS failure dataset consists of the daily operational sensor data from failed Scania trucks. The dataset is crucial to the manufacturer as it allows to isolate components which caused the failure. However, missing values and imbalanced class problems are the two most challenging limitations of this dataset to predict the cause of the failure. The prediction results can be affected by the way of handling these missing values and imbalanced class problem. In this paper, we have examined and presented the impact of five different missing value imputation techniques namely: Expectation Maximization, Mean Imputation, Soft Impute, MICE, and Iterative SVD in producing significantly better results. We have also performed an empirical comparison of their performance by applying five different classifiers namely: Naive Bayes, KNN, SVM, Random Forest, and Gradient Boosted Tree on this highly imbalanced dataset. The primary aim of this study is to observe the impact of the mentioned missing value imputation techniques in the enhancement of the prediction results, performing an empirical comparison to figure out the best classification model and imputation technique. We found that the MICE imputation and the random under-sampling techniques are the highest influential techniques for improving the prediction performance and false negative rate.

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An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data

Cited by 11 publications

References 3 publications

Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data

Averaging versus voting: A comparative study of strategies for distributed classification

An Empirical Comparison of Missing Value Imputation Techniques on APS Failure Prediction

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