Novel iterative approach using generative and discriminative models for classification with missing features

Kiasari, Mohammad Ahangar; Jang, Gil-Jin

doi:10.1016/j.neucom.2016.11.015

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Similarly, authors in [18] propose an adaptive incremental hybrid classifier to alleviate the impact of outliers in myoelectric pattern recognition. A more general-purpose framework is proposed by [19] where an iterative algorithm is used for classifying data with a missing feature. Although the proposed algorithm has been tested on different datasets and applications, it neither considers nor examines the effect of outliers on the classification task.…”

Section: Introductionmentioning

confidence: 99%

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

Iranfar¹,

Arza²,

Atienza³

2021

Preprint

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Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional monitoring devices is missing data due to sensors insufficient contact and interference by other equipment. This challenge becomes more problematic when the user/patient is mentally or physically active or stressed because of more frequent conscious or subconscious movements. In this paper, we propose ReLearn, a robust machine learning framework for stress detection from biomarkers extracted from multimodal physiological signals. ReLearn effectively copes with missing data and outliers both at training and inference phases. ReLearn, composed of machine learning models for feature selection, outlier detection, data imputation, and classification, allows us to classify all samples, including those with missing values at inference. In particular, according to our experiments and stress database, while by discarding all missing data, as a simplistic yet common approach, no prediction can be made for 34% of the data at inference, our approach can achieve accurate predictions, as high as 78%, for missing samples. Also, our experiments show that the proposed framework obtains a cross-validation accuracy of 86.8% even if more than 50% of samples within the features are missing.

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Section: Introductionmentioning

confidence: 99%

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

Iranfar¹,

Arza²,

Atienza³

2021

Preprint

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“…on human body model. This method can be broadly divided into the method based on generative models (GM) [13] and the method based on discriminative models (DM) [14], [15]. Chao et al [16] used conditional random field model (CRF) for behavior recognition.…”

Section: Introductionmentioning

confidence: 99%

Motion Recognition Algorithm in VR Video Based on Dual Feature Fusion and Adaptive Promotion

Han

2020

IEEE Access

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VR video recognition in complex environment, a motion recognition algorithm based on two-feature fusion and adaptive enhancement is proposed to solve the problems of inaccurate target position, target drift and recognition error caused by the vulnerability to light change, target rotation and occlusion. First, based on the spatio-temporal context (STC) mechanism, image sequence features are extracted through spatio-temporal context relationship and visual system characteristics to reduce the influence of light changes and occlusion on behaviors. Secondly, reliable feature point tracks are obtained through image feature point tracking and background track cutting, and a rich set of action descriptors (AD) are calculated from which local motion information, shape and static appearance information of the track are retained. After that, the principal component analysis operator is introduced to define the double feature fusion rules, and the STC feature and AD feature are combined to form a more accurate and complete feature representation. Finally, adaptive boosting algorithm (ABA) is used to train the classification through the new features obtained and complete the decision judgment of behavior and action. The experimental results show that the proposed algorithm has higher recognition accuracy and robustness compared with the current commonly used motion recognition methods, and can better adapt to complex background and motion changes.INDEX TERMS Motion recognition algorithm, VR video, dual feature fusion, adaptive promotion.

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“…The experimental results have shown that missing value imputation is a better choice than case deletion when the incomplete datasets contain a certain amount of missing values. Model-based missing value imputation algorithms based on machine learning techniques, such as k -nearest neighbor, multilayer perceptron neural networks, and support vector machines, have recently lately been widely considered [14, 16, 21]. …”

Section: Introductionmentioning

confidence: 99%

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Huang,

Lin,

Tsai

2018

Journal of Healthcare Engineering

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Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

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Novel iterative approach using generative and discriminative models for classification with missing features

Cited by 12 publications

References 16 publications

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

Motion Recognition Algorithm in VR Video Based on Dual Feature Fusion and Adaptive Promotion

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

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