A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification

Cao, Hong; Tan, Vincent Y. F.; Pang, John Z. F.

doi:10.1109/tnnls.2014.2308321

Cited by 45 publications

(20 citation statements)

References 32 publications

(81 reference statements)

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“…Among several methods, resampling techniques are frequently used to rebalance imbalanced datasets because they are independent of the selected classifier [30]. There are three types of resampling methods that have been used by researchers: over-sampling methods [31]- [33], under-sampling methods [34], [35], and hybrid methods [36]. Furthermore, according to a recent study [17], fraud detection is one of the most researched imbalanced learning topics.…”

Section: B Approaches For Addressing Imbalanced Datamentioning

confidence: 99%

An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

2021

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With the widespread of fake online reviews, the detection of fake reviews has become a hot research issue. Despite the efforts of existing studies on fake review detection, the issues of imbalanced data and feature pruning still lack sufficient attention. To address these gaps, the present study proposes an ensemble model for the detection of fake online reviews. The model consists of four steps, and the first three steps are proposed to optimize the base classifiers: (i) Data resampling: We propose a novel way to address the data imbalance problem by combining the resampling and the grid search technique. (ii) Feature pruning: We propose an ablation study to drop unimportant features. (iii) Parameters optimization: We apply the grid search algorithm to determine suitable values of the relevant parameters for each base classifier. (iv) Classifier ensembling: We apply majority voting and stacking strategies to integrate the optimized base classifiers. The proposed data resampling method is also applied for the meta-classifier in the stacking ensemble model. This study produces advances in terms of combining different methods or algorithms into a model and the results show that the proposed ensemble model outperforms some existing techniques, thereby providing a new way to solve the data imbalance and feature pruning issues in the field of fake review detection. INDEX TERMSData resampling, ensemble model, fake review, feature pruning, parameter optimization.

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Section: B Approaches For Addressing Imbalanced Datamentioning

confidence: 99%

An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

2021

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“…RQ4 can be divided into two more specific questions: RQ4.1, which concerns whether the research topics addressed by SLR articles in the current literature are comparatively few, and RQ4.2, [122] N N N Y 1.0 [55] N P N Y 1.5 [123] N P N Y 1.5 [124] N P N Y 1.5 [125] N P N Y 1.5 [104] N N N Y 1.0 [69] N N N Y 1.0 [126] N N N Y 1.0 [84] N P N Y 1.5 [127] N P N Y 1.5 [128] N P N Y 1.5 [129] N P N Y 1.5 which concerns the evidence that SLR studies on data preprocessing are lacking due to a lack of primary studies. A large number of studies in the existing literature have addressed data-related issues with an emphasis on data preprocessing.…”

Section: What Are the Limitations Of Current Research?mentioning

confidence: 99%

Systematic literature review of preprocessing techniques for imbalanced data

Felix

Lee

2019

IET softw.

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Data preprocessing remains an important step in machine learning studies. This is because proper preprocessing of imbalanced data can enable researchers to reduce defects as much as possible, which, in turn, may lead to the elimination of defects in existing data sets. Despite the remarkable achievements that have been accomplished in machine learning studies, systematic literature reviews of imbalanced data preprocessing techniques are lacking. Consequently, there are a limited number of systematic literature review studies on imbalanced data preprocessing. In this study, the authors assess the existing literature to identify the key issues related to data quality and handling and to provide a convenient collection of the techniques used to address these issues when performing data preprocessing. They applied a systematic literature review method involving a manual search to select articles published from January 2010 to September 2018 for review. The qualities of the existing studies were assessed using certain quality assessment criteria. Of the 118 relevant studies found, only 2% were identified as having been conducted following systematic literature review guidelines. This study, therefore, calls for more systematic literature review studies on data preprocessing to improve the quality of the data applied in machine learning studies.

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“…The model-based method assumes that time series in a class are generated by the same model, and the same category of data can be characterized by the same model parameters. Typical model-based methods are the ARMA model [29], the Gaussian mixture model [30], and the hidden Markov model [31]. The model-based method makes use of statistical characteristics of data, which is more informative and interpretable than methods based on distance and deep learning, and the computational efficiency is significantly higher than the three previous ones.…”

Section: Related Workmentioning

confidence: 99%

Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification

Wu¹,

Zhang²,

He³

et al. 2019

IEEE Access

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The multivariate time series (MTS) classification is an important classification problem in which data has the temporal attribute. Because relationships between many variables of the MTS are complex and time-varying, existing methods perform not well in MTS classification with many attribute variables. Thus, in this paper, we propose a novel model-based classification method, called Kullback-Leibler Divergence-based Gaussian Model Classification (KLD-GMC), which converts the original MTS data into two important parameters of the multivariate Gaussian model: the mean vector and the inverse covariance matrix. The inverse covariance is the most important parameter, which can obtain the information between the variables. So that the more variables, the more information could be obtained by the inverse covariance, KLD-GMC can deal with the relationship between variables well in the MTS. Then the sparse inverse covariance of each subsequence is solved by Graphical Lasso. Furthermore, the Kullback-Leibler divergence is used as the similarity measurement to implement the classification of unlabeled subsequences, because it can effectively measure the similarity between different distributions. Experimental results on classical MTS datasets demonstrate that our method can improve the performance of multivariate time series classification and outperform the state-of-the-art methods.

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A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification

Cited by 45 publications

References 32 publications

An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization

Systematic literature review of preprocessing techniques for imbalanced data

Learning Kullback-Leibler Divergence-Based Gaussian Model for Multivariate Time Series Classification

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