Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

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A Deep Analysis of the Precision Formula for Imbalanced Class Distribution

International Journal of Machine Learning and Computing ◽

10.7763/ijmlc.2014.v4.447 ◽

2014 ◽

Vol 4 (5) ◽

pp. 417-422 ◽

Cited By ~ 3

Author(s):

Gabriel Kofi Armah ◽

Guangchun Luo ◽

Ke Qin

Keyword(s):

Class Distribution ◽

Imbalanced Class ◽

Imbalanced Class Distribution

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Classification of Questions Based on Difficulty Levels using Support Vector Machine and Naïve Bayes Algorithms for Imbalanced Class

10.1109/ic2ie53219.2021.9649149 ◽

2021 ◽

Author(s):

Danny Naufal Pratama ◽

Oktariani Nurul Pratiwi ◽

Edi Sutoyo

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Imbalanced Class

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Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm

Genes ◽

10.3390/genes11070717 ◽

2020 ◽

Vol 11 (7) ◽

pp. 717

Author(s):

Garba Abdulrauf Sharifai ◽

Zurinahni Zainol

Keyword(s):

Feature Selection ◽

Optimization Algorithm ◽

Imbalanced Data ◽

High Dimensional ◽

Data Sets ◽

Biomedical Data ◽

Data Set ◽

Grasshopper Optimization Algorithm ◽

Imbalanced Class ◽

Grasshopper Optimization

The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

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