A new approach to dealing with missing values in data-driven fuzzy modeling

Almeida, Rui Jorge; Kaymak, Uzay; Sousa, J.M.

doi:10.1109/fuzzy.2010.5584894

Cited by 8 publications

(10 citation statements)

References 22 publications

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“…This means that the information contained in each time sample is related to the previous ones, as expected by physicians, otherwise the ZOH-FCM-PDS method would perform worse overall. Additionally, this reinforces the observations that for percentages of missing data up to approximately 70% the FCM-PDS model can perform well though the mean error increases as one approaches upper limits [11]. The method used in [4] performs similarly in terms of accuracy to FCM-PDS method.…”

Section: Resultssupporting

confidence: 85%

“…Additionally, both the global convergence theory and the local alternating optimization convergence theory are applicable [13]. Previous works [11] showed that the performance of these models is comparable to the one obtained when using a complete data set [11] making it a suitable tool for the present study.…”

Section: Introductionmentioning

confidence: 86%

“…TN TP Accuracy + + + + = (10) FN TP TP y Sensitivit + = (11) FP TN TN y Specificit + = (12) Where TP is the number of true positive classifications obtained, FP is the number of false positive classifications, TN is the number of true negative classifications and FN is the number of false negative classifications. The ROC curve is obtained by plotting the true positive classifications rate (TPR, or sensitivity) against the false positive classifications rate (FPR, or 1-specificity) obtained for a binary classifier system as its discrimination threshold is varied.…”

Section: Fn Tn Fp Tpmentioning

confidence: 99%

“…The set of optimal parameters with respect to the model output is estimated by minimizing the global prediction error, taking into account the aggregation of the rules [11].…”

Section: B Fuzzy Modeling With Missing Valuesmentioning

confidence: 99%

“…An alternative then to these two methods is to extend the data analysis method so that the method can deal directly with incomplete data. In this work we use a modified fuzzy c-means (FCM) clustering method that allows the identification of Takagi-Sugeno fuzzy models from incomplete data by means of product-space clustering [11]. This method uses the extension of the Fuzzy C-Means algorithm with the Partial Distance Strategy (FCM-PDS), discussed in [12,13], and does not require any kind of imputation or iterative guess-estimation of the missing values.…”

Section: Introductionmentioning

confidence: 99%

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Predicting septic shock outcomes in a database with missing data using fuzzy modeling: Influence of pre-processing techniques on real-world data-based classification

Pereira

Fialho

Cismondi

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with information that by design introduces bias. In this study, we show that predictive performance improves by employing a minimum pre-processing technique, the Zero-Order-Hold (ZOH) method, by applying a Fuzzy C-Means clustering technique based on the partial distance calculation strategy (FCM-PDS) and by computing the final classification regarding the samples from each patient. Performance improvements continue to occur where up to approximately 60% of the data is missing, though for higher percentage the classification performance still is statistically improved. We further validate this approach by making comparisons with previous studies.

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Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 86%

Section: Fn Tn Fp Tpmentioning

confidence: 99%

“…The set of optimal parameters with respect to the model output is estimated by minimizing the global prediction error, taking into account the aggregation of the rules [11].…”

Section: B Fuzzy Modeling With Missing Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting septic shock outcomes in a database with missing data using fuzzy modeling: Influence of pre-processing techniques on real-world data-based classification

Pereira

Fialho

Cismondi

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

Self Cite

View full text Add to dashboard Cite

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A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

Goh,

Ding,

Dai

et al. 2023

IEEJ Transactions Elec Engng

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Accurate wind power forecasting (WPF) is pivotal for the power system dominated by high penetration of renewable energy. Most forecasting techniques require sufficient data samples as a premise for achieving accurate prediction. Due to equipment faults during data collection, complete data is not always available, resulting that the forecasting accuracy is greatly diminished. To address this issue, this paper proposes a novel two‐stage hybrid forecasting approach including data restoration stage and forecasting stage. For the data restoration stage, the bidirectional long short‐term memory (Bi‐LSTM) is integrated into generative adversarial network (GAN) to recover the missing data with consideration of the complex time dynamics and correlations among heterogeneous data. To improve the prediction accuracy, the complete generated wind power sequence is decomposed into multiple time sequences with low volatility based on the enhanced variational mode decomposition (VMD). For the forecasting stage, a hybrid forecasting algorithm that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with an improved attention mechanism is proposed, strengthening the forecasting performance by assigning optimal weights to key features. The proposed hybrid forecasting method outperforms traditional methods based on real wind farm data with different shares of data loss from Guangxi province in China. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

Rahman

Davis

2013

Lecture Notes in Electrical Engineering

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A new approach to dealing with missing values in data-driven fuzzy modeling

Cited by 8 publications

References 22 publications

Predicting septic shock outcomes in a database with missing data using fuzzy modeling: Influence of pre-processing techniques on real-world data-based classification

Predicting septic shock outcomes in a database with missing data using fuzzy modeling: Influence of pre-processing techniques on real-world data-based classification

A Hybrid Short‐Term Wind Power Forecasting Model Considering Significant Data Loss

Machine Learning-Based Missing Value Imputation Method for Clinical Datasets

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