Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

Bourouis, Sami; Bouguila, Nizar

doi:10.1002/ima.22577

Cited by 4 publications

(2 citation statements)

References 75 publications

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“…In recent years, deep learning and machine learning technologies have gained prominence, with considerable impacts seen in real-world applications such as image/speech recognition, NLP, classification, prediction, and a wide variety of other applications [63]. The development of artificial neural networks has made these kinds of things conceivable in recent years.…”

Section: Significance Of Using Lstm For Automated Featuresmentioning

confidence: 99%

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

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Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

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Section: Significance Of Using Lstm For Automated Featuresmentioning

confidence: 99%

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Shah

Usman

Khalid

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Parametric approaches, however, are inappropriate for real machine learning problems as data sets grow over time under uncertainty and incompleteness. Thus, unlike parametric Bayesian approaches, which assume an unknown finite number of components, nonparametric Bayesian approaches guess infinitely complex models (i.e., an infinite number of components) [18,19] and have undergone significant theoretical and computational progress over the years [20,21]. It should also be noted that, in finite mixture models, estimating the optimal number of clusters is one of the difficult issues that can lead to an overfitting problem.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

et al. 2021

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This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.

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Expectation propagation learning of finite and infinite Gamma mixture models and its applications

Bourouis

Bouguila

2023

Multimed Tools Appl

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Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

Cited by 4 publications

References 75 publications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Expectation propagation learning of finite and infinite Gamma mixture models and its applications

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