A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Abdel-Naby, M.; Alfonse, Marco; Roushdy, Mohamed

doi:10.1007/978-981-16-2275-5_36

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Wrapper method uses a specific selection criterion to determine the quality of various feature subsets. Sequential forward and backward feature selection [23] and metaheuristic algorithms such as genetic algorithm [24] and particle swarm optimization [25] have been extensively used as wrapper algorithm for BC prediction. Dhanya et al [26] compared the FS and ML algorithms for BC prediction.…”

Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Akkur¹,

TURK²,

Eroğul³

2023

Computer Systems Science and Engineering

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Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were implemented to obtain the best classification process. Relief, Least Absolute Shrinkage and Selection Operator (LASSO) and Sequential Forward Selection were used to determine the most relevant features, respectively. The machine learning models were optimized with the help of Bayesian optimization approach to obtain optimal hyperparameter values. Experimental results showed the unified feature selection-hyperparameter optimization method improved the classification performance in all machine learning algorithms. Among the various experiments, LASSO-BO-SVM showed the highest accuracy, precision, recall and F1-score for two datasets (97.95%, 98.28%, 98.28%, 98.28% for MBCD and 98.95%, 97.17%, 100%, 98.56% for MBCD), yielding outperforming results compared to recent studies.

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Section: Literature Surveymentioning

confidence: 99%

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Akkur¹,

TURK²,

Eroğul³

2023

Computer Systems Science and Engineering

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“…The data consist of 2,905 genes with only 168 samples. Recent studies show that many researchers used breast cancer data to tackle the problem of highdimensional data [14], [15]. The second data will be used in prostate cancer, which was initially analyzed by [10].…”

Section: Data Acquisitionmentioning

confidence: 99%

Hybrid filtering methods for feature selection in high-dimensional cancer data

Md Noh,

Ibrahim,

Mansor

et al. 2023

IJECE

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Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain.

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Computational Microarray Gene Selection Model Using Metaheuristic Optimization Algorithm for Imbalanced Microarrays Based on Bagging and Boosting Techniques

Elden

Ghoneim

Hadhoud

et al. 2022

Lecture Notes in Computer Science

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A Hybrid Mutual Information-LASSO-Genetic Algorithm Selection Approach for Classifying Breast Cancer

Cited by 5 publications

References 32 publications

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

Hybrid filtering methods for feature selection in high-dimensional cancer data

Computational Microarray Gene Selection Model Using Metaheuristic Optimization Algorithm for Imbalanced Microarrays Based on Bagging and Boosting Techniques

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