Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

Sim, Jaemun; Lee, Jonathan Sangyun; Kwon, Ohbyung

doi:10.1155/2015/538613

Cited by 61 publications

(28 citation statements)

References 21 publications

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“…First, the scores were calculated by using just the answers to the questions from a subset and setting the remaining questions to the frequency “rarely or never”. This is actually the so-called zero imputation method [ 12 ]. An alternative approach was also used, specifically multiple imputation [ 13 , 14 ].…”

Section: Methodsmentioning

confidence: 99%

Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model

et al. 2020

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Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques. FFQs can get very extensive, which could indicate that some of the questions are less significant than others and could be omitted without losing too much information. In this paper, machine learning is used to explore how reducing the number of questions affects the predicted nutrient values and diet quality score. The paper addresses the problem of removing redundant questions and finding the best subset of questions in the Extended Short Form Food Frequency Questionnaire (ESFFFQ), developed as part of the H2020 project WellCo. Eight common machine-learning algorithms were compared on different subsets of questions by using the PROMETHEE method, which compares methods and subsets via multiple performance measures. According to the results, for some of the targets, specifically sugar intake, fiber intake and protein intake, a smaller subset of questions are sufficient to predict diet quality scores. Additionally, for smaller subsets of questions, machine-learning algorithms generally perform better than statistical methods for predicting intake and diet quality scores. The proposed method could therefore be useful for finding the most informative subsets of questions in other FFQs as well. This could help experts develop FFQs that provide the necessary information and are not overbearing for those answering.

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Section: Methodsmentioning

confidence: 99%

Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model

et al. 2020

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“…They encompass anything from simple, like univariate mean/mode imputation to more complex multivariate schemes that look for relationships among covariates. Many studies have compared the performance of imputation methods but, unfortunately, regardless of the simplicity or complexity of an imputation method; its execution will always depend on the fitness between the data set, imputation method, and characteristics of the missing data (Sim et al, 2015).…”

Section: Missing Value Imputationmentioning

confidence: 99%

Tree-Based Missing Value Imputation Using Feature Selection

Rosado-Galindo¹,

Dávila-Padilla²

2021

Journal of Data Science

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Researchers and practitioners of many areas of knowledge frequently struggle with missing data. Missing data is a problem because almost all standard statistical methods assume that the information is complete. Consequently, missing value imputation offers a solution to this problem. The main contribution of this paper lies on the development of a random forest-based imputation method (TI-FS) that can handle any type of data, including high-dimensional data with nonlinear complex interactions. The premise behind the proposed scheme is that a variable can be imputed considering only those variables that are related to it using feature selection. This work compares the performance of the proposed scheme with other two imputation methods commonly used in literature: KNN and missForest. The results suggest that the proposed method can be useful in complex scenarios with categorical variables and a high volume of missing values, while reducing the amount of variables used and their corresponding preliminary imputations.

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“…Spearman correlation method is used in improved imputation method to find the missing values and the performance of classification is given in RoC curve for the methods SVM, NB and KNN Elenita et al (2019), Ghorbani and Desmarais (2017;Schmitt et al, 2015). Data set with missing values influence the algorithm by weaken it and reduce the accuracy (Sim et al (2015;Kanchana and Thanamani, 2016). For classification problems, Naive Bayes classifier and SVM (Cristianini and Shawe-Taylor, 2000) are used in widespread.…”

Section: Introductionmentioning

confidence: 99%

Missing Values Treatment and Feature Reduction Analysis to Enhance Classification

Muralidharan

Renuka

Jaswant

et al. 2020

Journal of Computer Science

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Datasets may have large number of features which makes it hard and time consuming to classify. Additionally, they may have irrelevant and noise features too with missing values. The missing values should be treated in a proper way so that the classifier accuracy can be improved. There is also a need to reduce features and select only the features necessary to the classifier. Principal Component Analysis (PCA) is commonly considered for this process of reducing the number of features in a dataset. These reduced components can be applied as input to the classifiers. In this study, standard datasets are checked for missing values, classified using Support vector Machines (SVM) and Naive Bayes with and without reducing the features using PCA. Then, the proposed algorithm for missing value imputation is used on the datasets and the same analysis were carried out. The accuracy is evaluated using Confusion Matrix. The results are discussed with analysis based on the nature of features and missing values and how different datasets behave when used with machine learning algorithms.

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Missing Values and Optimal Selection of an Imputation Method and Classification Algorithm to Improve the Accuracy of Ubiquitous Computing Applications

Cited by 61 publications

References 21 publications

Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model

Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model

Tree-Based Missing Value Imputation Using Feature Selection

Missing Values Treatment and Feature Reduction Analysis to Enhance Classification

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