Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Vong, Chi‐Man; Ip, Weng Fai; Chiu, Chi-Chong; Wong, Pak-Kin

doi:10.1007/s12559-014-9301-0

Cited by 25 publications

(6 citation statements)

References 31 publications

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“…However, among these primary studies, there is sufficient evidence that SLR studies on data preprocessing are lacking, as indicated by the fact that only 2% of the primary studies considered in this study followed SLR guidelines. This finding [136] N P N Y 1.5 [105] N N N Y 1.0 [86] N Y N Y 2.0 [137] N N N Y 1.0 [106] N P N Y 1.5 [83] N Y N Y 2.0 [138] N P N Y 1.5 [139] N P N Y 1.5 [140] N N N Y 1.0 [68] N Y N Y 2.0 [141] N P N Y 1.5 [142] N P N Y 1.5 [67] N P N Y 1.5 [90] N P N Y 1.5 [50] N P N Y 1.5 [53] N N N Y 1.0 [52] N P N Y 1.5 [47] N P N Y 1.5 [48] N N N Y 1.0 [143] N P N Y 1.5 [117] N Y N Y 2.0 [144] N N N Y 1.0 [145] N P N Y 1.5 [73] Y Y Y Y 4.0 [146] N P N Y 1.5 [147] N P N Y 1.5 [148] N N N Y 1.0 [149] N P N Y 1.5 [150] N P N Y 1.5 [60] N P N Y 1.5 [151] N N N Y 1.0 [152] N P N Y 1.5 [153] N N N Y 1.0 [154] N N N Y 1.0 [155] N N N Y 1.0 [156] N N N Y 1.0 [157] N N N Y 1.0 [158] N P N Y 1.5 [159] N N N Y 1.0 [160] N N N Y 1.0 [161] N N N Y 1.0 [162] N N N Y 1.0…”

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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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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“…However, when applying these methods in practical tasks, their performance has not always been acceptable because of their imbalanced training sets, i.e., because of the skewness of the sample distribution across classes. In many practical disciplines, including chemical and biomedical engineering [1], financial engineering [2], environmental sciences [3] and information technology [4], rare events, also referred to as abnormal behaviors, are of great importance. Therefore, it is critical to reduce the influence of the bias introduced from training sets on the detection of rare events.…”

Section: Introductionmentioning

confidence: 99%

Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Liu

Heiselman

Quirk

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Classification with imbalanced data is a common and challenging problem in many practical machine learning problems. Ensemble learning is a popular solution where the results from multiple base classifiers are synthesized to reduce the effect of a possibly skewed distribution of the training set. In this paper, binary classifiers based on Gaussian processes are chosen as bases for inferring the predictive distributions of test latent variables. We apply a Gaussian process latent variable model where the outputs of the Gaussian processes are used for making the final decision. The tests of the new method in both synthetic and real data sets show improved performance over standard approaches.

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“…In ICDM'05, CIL problem has also been listed into the Top 10 challenging problems in data mining [8]. During the past two decades, CIL problem has been observed in a lot of real-world applications, including fraud detection [9], [10], network intrusion detection [11], [12], disease diagnosis [13], [14], software defect detection [15], [16], industrial manufacturing [17], Bioinformatics [18], [19], environment resource management [20], and security management [21], etc.…”

Section: Introductionmentioning

confidence: 99%

Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

Zhang

et al. 2019

IEEE Access

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SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent. However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter K but neglects the local distribution characteristics. To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article. The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively. Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes. Next, two new GMMs are constructed on the rest majority and minority class instances, respectively. Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information. Finally, we assign an individual parameter K to the instances belonging to each specific group to generate new instances. We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms. The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.

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Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Cited by 25 publications

References 31 publications

Systematic literature review of preprocessing techniques for imbalanced data

Systematic literature review of preprocessing techniques for imbalanced data

Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models

Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

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