GT2FS-SMOTE: An Intelligent Oversampling Approach Based Upon General Type-2 Fuzzy Sets to Detect Web Spam

Kaur, Prabhjot; Gosain, Anjana

doi:10.1007/s13369-020-04995-5

Cited by 12 publications

(6 citation statements)

References 96 publications

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“…The method does not differentiate between overlapping class groups from so called safe regions due to which it results in the generation of noise and problem of over-generalization [24,25]. It also fails to overcome the problem of small disjoints [26].…”

Section: Synthetic Minority Over-sampling Technique (Smote) and Exten...mentioning

confidence: 99%

MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data

Arora¹,

Tushir²,

Sharma³

et al. 2022

Computers, Materials &Amp; Continua

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Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different undersampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To overcome these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known as MCBC-SMOTE (Majority Clustering for balanced Classification-SMOTE). The model provides a method to convert the problem of binary classification into a multi-class problem. In the proposed algorithm, the number of clusters for the majority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution. The proposed technique is cost-effective, reduces the problem of noise generation and successfully disables the imbalances present in between and within classes. The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics.

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Section: Synthetic Minority Over-sampling Technique (Smote) and Exten...mentioning

confidence: 99%

MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data

Arora¹,

Tushir²,

Sharma³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This technique has shown good results compared to other techniques, but it is difficult to be applied because of its long procedure. A general type‐2 fuzzy sets (GT2FS‐SMOTE) approach has been introduced by Kaur and Gosain 18 . It first applies type‐2 of fuzzy clustering to the data.…”

Section: Related Workmentioning

confidence: 99%

“…A general type-2 fuzzy sets (GT2FS-SMOTE) approach has been introduced by Kaur and Gosain. 18 It first applies type-2 of fuzzy clustering to the data. After that, the minority class is balanced by using SMOTE on the classes, having a very high type-2 membership.…”

Section: Related Workmentioning

confidence: 99%

High performance oversampling technique considering intra‐class and inter‐class distances

Keskes

Kobbi-Fakhfakh

Kanoun

et al. 2021

Concurrency and Computation

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Oversampling methods are among the most widely used methods to solve imbalanced data analysis due to their simplicity and flexibility. Multi-class imbalanced problems involve critical issues related to synthetic samples and corresponding consequences on the classification results. The over-fitting phenomenon, the overlapping regions between classes, and the existence of noisy original samples may lead to a non-optimal data distribution, which inadequately affects the classifier performance.To avoid these limitations, we propose a novel oversampling technique based on the intra-distance-matrix (IntraDM) and the inter-distance-matrix (InterDM). The new samples are created to decrease over-fitting, decline the generation of outliers, and minimize the overlap areas. Applying the proposed method on 14 available datasets shows that it outperforms common oversampling techniques in terms of classification evaluation metrics.

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“…However, the proposed NM-BPNN outperforms the original BPNN method. Kaur et al [21], proposed oversampling technique name General type-2 fuzzy set-based SMOTE (GT2FS-SMOTE) for web spam detection. The GT2FS-SMOTE identifies the minority samples that are close to the center for oversampling.…”

Section: Related Workmentioning

confidence: 99%

ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

Barot¹,

Jethva²

2021

ijisae

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A reliable and precise tool for medical machine learning is in demand. The diagnosis datasets are mostly unbalanced. To propose an accurate prediction tool for medical data we need an accurate machine-learning algorithm for unbalanced data classification. In binary class unbalanced medical dataset, accurate prediction of the minority class is important. Traditional classifiers designed to improve accuracy by giving more weight to the majority class. Existing techniques gives good results by accurately classifying the majority class. Despite the fact that they misclassify the minority cases, the total accuracy value does not reflect this. When the misclassification cost of minority class is high, research should focus on reducing the total misclassification cost. This paper presents a new cost-sensitive classification algorithm that classifies unbalanced data accurately without compromising the accuracy of the minority class. Our proposed minority-sensitive decision tree algorithm employs new splitting criteria called MSplit to ensure accurate prediction of the minority class. The proposed splitting criteria MSplit derived from the exclusive causes of the minority class. For our experiment, we mainly focused on the breast cancer dataset by considering its importance in women's health. Our proposed model shows good results as compared to the recent studies of breast cancer detection. It shows 0.074 misclassification cost that is the least among the other comparison methods. Our model improves the performance for other unbalanced medical datasets as well.

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GT2FS-SMOTE: An Intelligent Oversampling Approach Based Upon General Type-2 Fuzzy Sets to Detect Web Spam

Cited by 12 publications

References 96 publications

MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data

MCBC-SMOTE: A Majority Clustering Model for Classification of營mbalanced Data

High performance oversampling technique considering intra‐class and inter‐class distances

ImbTree: Minority Class Sensitive Weighted Decision Tree for Classification of Unbalanced Data

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