• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.139-142
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• 1998

Missing values in data for fuzzy c-menas clustering is discussed. Two basic methods of fuzzy c-means, i.e., the standard fuzzy c-means and the entropy method are considered and three options of handling missing values are proposed, among which one is to define a new distance between data with missing values, second is to alter a weight in the new distance, and the third is to fill the missing values by an appropriate numbers. Experimental Results are shown.

• International Journal of Contents
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• 제9권4호
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• pp.1-10
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• 2013

The electroencephalogram (EEG) time series is a measure of electrical activity received from multiple electrodes placed on the scalp of a human brain. It provides a direct measurement for characterizing the dynamic aspects of brain activities. These EEG signals are formed from a series of spatial and temporal data with multiple dimensions. Missing data could occur due to fault electrodes. These missing data can cause distortion, repudiation, and further, reduce the effectiveness of analyzing algorithms. Current methodologies for EEG analysis require a complete set of EEG data matrix as input. Therefore, an accurate and reliable imputation approach for missing values is necessary to avoid incomplete data sets for analyses and further improve the usage of performance techniques. This research proposes a new method to automatically recover random consecutive missing data from real world EEG data based on Linear Dynamical System. The proposed method aims to capture the optimal patterns based on two main characteristics in the coevolving EEG time series: namely, (i) dynamics via discovering temporal evolving behaviors, and (ii) correlations by identifying the relationships between multiple brain signals. From these exploits, the proposed method successfully identifies a few hidden variables and discovers their dynamics to impute missing values. The proposed method offers a robust and scalable approach with linear computation time over the size of sequences. A comparative study has been performed to assess the effectiveness of the proposed method against interpolation and missing values via Singular Value Decomposition (MSVD). The experimental simulations demonstrate that the proposed method provides better reconstruction performance up to 49% and 67% improvements over MSVD and interpolation approaches, respectively.

• 한국정보전자통신기술학회논문지
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• 제17권3호
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• pp.176-182
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• 2024

본 연구는 결측치 비율이 높은 시계열 데이터를 효과적으로 분석하고 예측할 수 있는 머신러닝 모델을 구축하기 위해 다양한 결측치 처리 방법을 비교 분석하였다. 이를 위해 PSMF(Predictive State Model Filtering), MissForest, IBFI(Imputation By Feature Importance) 방법을 적용하였으며, 이후 LightGBM, XGBoost, EBM(Explainable Boosting Machines) 머신러닝 모델을 사용하여 예측 성능을 평가하였다. 연구 결과, 결측치 처리 방법 중에서는 MissForest와 IBFI가 비선형적 데이터 패턴을 잘 반영하여 가장 높은 성능을 나타냈으며, 머신러닝 모델 중에서는 XGBoost와 EBM 모델이 LightGBM 모델보다 더 높은 성능을 보였다. 본 연구는 결측치 비율이 높은 시계열 데이터의 분석 및 예측에 있어 비선형적 결측치 처리 방법과 머신러닝 모델의 조합이 중요함을 강조하며, 실무적으로 유용한 방법론을 제시하였다.

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.331-341
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• 2023

Handling missing values in data analysis is essential in constructing a good prediction model. The easiest way to handle missing values is to use complete case data, but this can lead to information loss within the data and invalid conclusions in data analysis. Imputation is a technique that replaces missing data with alternative values obtained from information in a dataset. Conventional imputation methods include K-nearest-neighbor imputation and multiple imputations. Recent methods include missForest, missRanger, and mixgb ,all which use machine learning algorithms. This paper compares the imputation techniques for datasets with mixed datatypes in various situations, such as data size, missing ratios, and missing mechanisms. To evaluate the performance of each method in mixed datasets, we propose a new imputation performance measure (IPM) that is a unified measurement applicable to numerical and categorical variables. We believe this metric can help find the best imputation method. Finally, we summarize the comparison results with imputation performances and computational times.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.105-110
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference. data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values.. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• 지능정보연구
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• 제9권2호
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• Journal of information and communication convergence engineering
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• 제1권3호
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• pp.150-156
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• 2003

This paper explored the use of an algorithm for the data mining and method in handling missing data which had generated enhanced association patterns observed using the data illustrated here. The evaluations showed that more association patterns are generated in the second analysis which suggests more meaningful rules than in the first situation. It showed that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, strategies could be efficiently planned out and implemented to improve marketing schemes. This investigation gives rise to a number of interesting issues that could be explored further like the effect of outliers and missing data for detecting fraud and devious database entries.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.399-411
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• 2004

A common method of handling nonresponse in sample survey is to delete the cases, which may result in a substantial loss of cases. Thus in certain situation, it is of interest to create a complete set of sample values. In this case, a popular approach is to impute the missing values in the sample by the mean or the median of responders. The difficulty with this method which just replaces each missing value with a single imputed value is that inferences based on the completed dataset underestimate the precision of the inferential procedure. Various suggestions have been made to overcome the difficulty but they might not be appropriate for public-use files where the user has only limited information for about the reasons for nonresponse. In this note, a multiple imputation method is considered to create complete dataset which might be used for all possible inferential procedures without misleading or underestimating the precision.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.105-113
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• 2002

The statistical analysis of incomplete data sometimes requires handling of incomplete observations. Towards this end, each case with some missing values generally should be deleted, namely, resulting in only use of non-missing cases. EM algorithm(Dempster et al., 1977) which involves prediction and estimation steps is a general method among others. In this article, we use the free software NORM developed for multiple imputation, which uses DA(Data Augmentation) algorithm in its imputation, and evaluate its efficiency through a numerical example.

• 한국정보통신학회논문지
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• 제14권1호
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• pp.53-62
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• 2010

본 논문은 변수 값들이나 부류 값을 손실한, 불완전한 데이터를 포함하는 데이터 집합을 가지고 학습하는 문제에 적용될 수 있는 분류 알고리즘을 소개한다. 이 알고리즘은 가중치 값과 확률 기법들을 이용하는 데이터 확장 방법을 사용한다. 이는 휘셔(Fisher)의 식을 기반으로 최적의 투사 면이 되도록 고려된 분류기를 확장함으로써 수행한다. 이를 위해, 데이터 확장에 적용되는 과정으로 부터 몇몇 식들이 유도된다. 제안한 알고리즘의 성능평가를 위해, 데이터에서 하나의 변수를 선택하고 이 선택된 변수에 소실 값과 소실되지 않은 값들의 비율을 변형함에 의해 다른 측정값들의 결과들이 반복적으로 비교된다. 또한 데이터 집합의 객관적인 평가를 위해 기계학습에서 지식 습득 도구로 널리 쓰이는 C4.5의 결과와 비교한다.