Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Patil, Sachin; Sonavane, Shefali

doi:10.1186/s40537-017-0108-1

Cited by 7 publications

(2 citation statements)

References 40 publications

(40 reference statements)

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“…Linear Support Vector Classifier LSVC [48] was considered to measure the performance of the proposed feature selection method and traditional feature selection methods on multi-class imbalanced data. Also, accuracy [49], precision [50], recall [51], and f-measure [52] were used to assess the performance of the LSVC classifier over feature selection methods. The proposed feature selection method was evaluated over four learning classifiers such as LSVC, Naïve Bayes (NB) [53], Logistic Regression (LR) [54], and Random Forest (RF) [55].…”

Section: Resultsmentioning

confidence: 99%

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

Kodabagi²

2022

MJCS

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Feature selection plays a prominent role in email classification since selecting the most relevant features enhances the accuracy and performance of the learning classifier. Due to the exponential increase rate in the usage of emails, the classification of such emails posed a fitting problem. Therefore, there is a requirement for a proper classification system. Such an email classification system requires an efficient feature selection method for the accurate classification of the most relevant features. This paper proposes a novel feature selection method for sentence similarity using the longest common subsequence for email classification. The proposed feature selection method works in two main phases: First, it builds the longest common subsequence vector of features by comparing each email with all other emails in the dataset. Later, a template is constructed for each class using the closest features of emails of a particular class. Further, email classification is tested for unseen emails using these templates. The performance of the proposed method is compared with traditional feature selection methods such as TF-IDF, Information Gain, Chi-square, and semantic approach. The experimental results showed that the proposed method performed well with 96.61% accuracy.

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Section: Resultsmentioning

confidence: 99%

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

Kodabagi²

2022

MJCS

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“…Previous literary works proposed that the solution to these issues can be provided by implementing data sampling approaches and advanced learning algorithms trained by machine learning and deep learning techniques. These algorithms are developed for handling imbalanced data effectively (Patil and Sonavane, 2017). The data sampling methodologies are broadly categorized into two categories such as undersampling and oversampling (Haixiang et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

Hemalatha

Amalanathan

2021

IJICC

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PurposeAdequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance. The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset. This issue is known as the imbalance problem, which is one of the most common issues occurring in real-time applications. Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining. Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approachIn the proposed work, a novel fuzzy-based Gaussian synthetic minority oversampling (FG-SMOTE) algorithm is proposed to process the imbalanced data. The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets. The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.FindingsThe performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC: 93.7%. F1 Score Prediction: 94.2%, Geometric Mean Score: 93.6% predicted from confusion matrix.Research limitations/implicationsThe proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/valueThe proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data. FG-SMOTE has aided in balancing minority and majority class datasets.

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Oversampled Deep Fully Connected Neural Network Towards Improving Classifier Performance for Fraud Detection

Devi

Pandian

Ramesh

et al. 2022

Advances in Data and Information Sciences

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Improved classification of large imbalanced data sets using rationalized technique: Updated Class Purity Maximization Over_Sampling Technique (UCPMOT)

Cited by 7 publications

References 40 publications

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

Feature Engineering With Sentence Similarity Using the Longest Common Subsequence for Email Classification

FG-SMOTE: Fuzzy-based Gaussian synthetic minority oversampling with deep belief networks classifier for skewed class distribution

Oversampled Deep Fully Connected Neural Network Towards Improving Classifier Performance for Fraud Detection

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