Mixture of Gaussians for distance estimation with missing data

Eirola, Emil; Lendasse, Amaury; Vandewalle, Vincent; Biernacki, Christophe

doi:10.1016/j.neucom.2013.07.050

Cited by 60 publications

(40 citation statements)

References 23 publications

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“…One of the most accepted model-based methods is the mixture models trained with expectation-maximization algorithm [28,5]. The fourth method is the machine learning method for handling missing data, which aims to develop machine learning techniques that are more robust to missing data incidence.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

An Evolutionary Missing Data Imputation Method for Pattern Classification

Lobato

Tadaiesky

Araújo

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Data analysis plays an important role in our InformationEra; however, most of statistical and machine learning algorithms were not developed to tackle the ubiquitous issue of missing values. In pattern classification, several strategies have been proposed to handle this problem, where missing data imputation is the most used one, which can be viewed as an optimization problem where the goal is to reduce the bias imposed by the absence of information. Although most imputation methods are restricted to one type of variable only (categorical or numerical), they usually ignore information within incomplete instances. To fill these gaps, we propose an evolutionary missing data imputation method for pattern classification, based on a genetic algorithm, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and model building -more specifically, the classification accuracy. To assess the performance of our method, we used three algorithms in order to represent the three groups of classification methods: 1) rule induction learning, 2) approximate models and 3) lazy learning. Experiments have shown that the proposed method outperforms some well-established missing value treatment methods.

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Section: Theoretical Backgroundmentioning

confidence: 99%

An Evolutionary Missing Data Imputation Method for Pattern Classification

Lobato

Tadaiesky

Araújo

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…AIC C is susceptible to high dimensional data, with high dimensional datasets, validity of AIC C vanishes. 15 To overcome this problem, a clustering based method High Dimensional Data Clustering (HDDC) 16 is utilized.…”

Section: Methodsmentioning

confidence: 99%

ANFIS and Deep Learning based missing sensor data prediction in IoT

Guzel

Kök

Akay

et al. 2019

Concurrency and Computation

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Summary Internet of Things (IoT) consists of billions of devices that generate big data which is characterized by the large volume, velocity, and heterogeneity. In the heterogeneous IoT ecosystem, it is not so surprising that these sensor‐generated data are considered to be noisy, uncertain, erroneous, and missing due to the lack of battery power, communication errors, and malfunctioning devices. This paper presents Deep Learning (DL) and Adaptive‐Network based Fuzzy Inference System (ANFIS) based prediction models for missing sensor data problem in IoT ecosystem. First, we build ANFIS based models and optimize their parameters. Then, we construct DL based models by using Long Short Term Memory (LSTM) network structure and optimize its parameters by applying the grid search method. Finally, we evaluate all the proposed models with Intel Berkeley Lab dataset. Experimental results demonstrate that the proposed models can significantly improve the prediction accuracy and may be promising for missing sensor data prediction.

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“…The EM algorithm for fitting a Gaussian mixture model has been extended to handle such data in a natural way [22], [23], [24].…”

Section: F Missing Valuesmentioning

confidence: 99%

Variable selection for regression problems using Gaussian mixture models to estimate mutual information

Eirola

Lendasse

Karhunen

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Variable selection is a crucial part of building regression models, and is preferably done as a filtering method independently from the model training. Mutual information is a popular relevance criterion for this, but it is not trivial to estimate accurately from a limited amount of data. In this paper, a method is presented where a Gaussian mixture model is used to estimate the joint density of the input and output variables, and subsequently used to select the most relevant variables by maximising the mutual information which can be estimated using the model.

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Mixture of Gaussians for distance estimation with missing data

Cited by 60 publications

References 23 publications

An Evolutionary Missing Data Imputation Method for Pattern Classification

An Evolutionary Missing Data Imputation Method for Pattern Classification

ANFIS and Deep Learning based missing sensor data prediction in IoT

Variable selection for regression problems using Gaussian mixture models to estimate mutual information

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